scholarly journals Chains of Spatial and Temporal Precipitation Occurrence Predictability Across the Continental U.S.

2021 ◽  
Vol 3 ◽  
Author(s):  
Allison Goodwell ◽  
Ritzwi Chapagain
Keyword(s):  
Information Sources ◽  
Daily Precipitation ◽  
Seasonal Patterns ◽  
Grid Cells ◽  
Precipitation Occurrence ◽  
Temporal Properties ◽  
Daily Precipitation Data ◽  
Different Seasons ◽  
Original Cell ◽  
Chain Lengths

Both spatial and temporal information sources contribute to the predictability of precipitation occurrence at a given location. These sources, and the level of predictability they provide, are relevant to forecasting and understanding precipitation processes at different time scales. We use information theory-based measures to construct connected “chains of influence” of spatial extents and timescales of precipitation occurrence predictability across the continental U.S, based on gridded daily precipitation data. These regions can also be thought of as “footprints” or regions where precipitation states tend to be most synchronized. We compute these chains of precipitation influence for grid cells in the continental US, and study metrics regarding their lengths, extents, and curvature for different seasons. We find distinct geographic and seasonal patterns, particularly longer chain lengths during the summer that are indicative of larger spatial extents for storms. While synchronous, or instantaneous, relationships are strongest for grid cells in the same region, lagged relationships arise as chains reach areas farther from the original cell. While this study focuses on precipitation occurrence predictability given only information about precipitation, it could be extended to study spatial and temporal properties of other driving factors.

scholarly journals Mediterranean cyclone characteristics related to precipitation occurrence in Crete, Greece

2015 ◽  
Vol 15 (8) ◽  
pp. 1807-1819 ◽  
Author(s):  
V. Iordanidou ◽  
A. G. Koutroulis ◽  
I. K. Tsanis
Keyword(s):  
Propagation Velocity ◽  
Daily Precipitation ◽  
Variable Intensity ◽  
Data Set ◽  
Precipitation Occurrence ◽  
Crete Island ◽  
Daily Precipitation Data ◽  
Cyclone Tracks ◽  
Western Side ◽  
Precipitation Events

Abstract. The characteristics of the cyclone tracks that caused precipitation events of variable intensity for the period 1979–2011 over the island of Crete are presented. The data set used for cyclone identification is the 0.5° × 0.5°, 30 years European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim mean sea-level pressure. Cyclone characteristics are calculated with the aid of the Melbourne University algorithm (MS scheme). Daily precipitation data from a dense gauging network over the island of Crete are also used for the classification of the precipitation events in terms of rain accumulation (intensity). Daily precipitation is classified in three categories and the associated cyclones are chosen according to their distance from Crete island. The seasonal and annual cycle of the physical characteristics of the cyclone tracks are investigated with respect to the cyclones' relative position to the island of Crete. It was found that cyclones affecting Crete most frequently approach from the western side of the island and the actual cyclone centers associated with precipitation events are usually located northwest and southeast of the Crete domain. Cyclone-induced rainfall increases in function to cyclones' depth, radius and propagation velocity increase as well as cyclones' pressure decrease. Spring cyclones that affect Crete with rainfall present lower pressures and higher cyclone propagation velocity in contrast to the ones associated with winter and autumn precipitation events. The examination of the relation between cyclone characteristics and precipitation occurrence provides valuable information related to forecasting potential and management of the water resources and the rainfall extremes.

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 Mediterranean depression characteristics related to precipitation occurrence in Crete, Greece

2014 ◽  
Vol 2 (9) ◽  
pp. 6107-6139 ◽  
Author(s):  
V. Iordanidou ◽  
A. G. Koutroulis ◽  
I. K. Tsanis
Keyword(s):  
Daily Precipitation ◽  
Morphological Characteristics ◽  
Variable Intensity ◽  
Weather Forecasts ◽  
Precipitation Occurrence ◽  
Seasonal Characteristics ◽  
Crete Island ◽  
Daily Precipitation Data ◽  
Ms Scheme ◽  
Precipitation Events

Abstract. The characteristics of the cyclone tracks and circulation patterns that caused precipitation events of variable intensity for the period 1979–2011 over the island of Crete are presented. The dataset usedfor cyclone identification, is the 0.5 x 0.5, 30 years European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim mean sea-level pressure. Their characteristics are extracted with the aid of Melbourne University algorithm (MS scheme). Daily precipitation data from a dense gauging network over the island of Crete is also used for the classification of the precipitation events in terms of intensity. Daily precipitation intensity is classified in three severity categories, and the associated cyclones are filtered according to their distance from Crete Island. The atmospheric systems are further investigated both seasonally and annually for their position relative to Crete and morphological characteristics such as intensity and radius. Generally, it was found that cyclones affecting Crete most frequently approach from northwest and southwest directions and the actual cyclone centers associated with precipitation events are usually located in northwest and southeast positions relative to Crete domain. Precipitation increase is observed in parallel with cyclone pressure decrease as well as cyclone intensity, depth, radius and propagation velocity increase. Specific seasonal characteristics such as lower pressures and cyclone radius can be detected in spring in contrast to winter and autumn precipitation events. The examination of the relation between cyclone characteristics and precipitation occurrence provides improved understanding of the complex hydro-meteorological conditions and therefore valuable hydrologic information related to forecasting potential and management of the resources and the extremes.

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 Temporal and Spatial Variations of Precipitation Change from Southeast to Northwest China during the Period 1961–2017

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2622
Author(s):  
Zhu Li ◽  
Honghu Liu
Keyword(s):  
Daily Precipitation ◽  
Global Climate ◽  
Heavy Rain ◽  
Northwest China ◽  
Annual Rainfall ◽  
Trend Test ◽  
Spatiotemporal Variations ◽  
Light Rain ◽  
Daily Precipitation Data ◽  
Northwest Region

Global climate change is significant, and the spatiotemporal variations of precipitation associated with it are pronounced. Based on the daily precipitation data from 10 weather stations located from southeast to northwest across China from 1961–2017, the Mann–Kendall trend test was generally applied to analyze the spatiotemporal variations of precipitation. The factors influencing the precipitation changes were investigated. The results revealed that (1) the annual, summer, and winter rainfall amount (RA) exhibited increasing rates of 16.36, 12.31, and 2.49 mm/10 year, respectively. The change rates of annual rainfall days (RD) were 2.68 day/10 year in the northwest region and −1.88 day/10 year in the southeast. The annual and seasonal daily precipitation on rainy days (RP) exhibited an increasing trend. (2) All of the RA, RD, and RP values initially increased, then decreased, and then slightly increased from Southeast to Northwest China. These results proved that the RA increased with the increase of light rain in Northwest China and heavy rain in Southeast China. In addition, changes in the monsoon have altered the rate at which RA, RD, and RP vary with distance from the sea. These findings may help to provide suggestions for the rational spatial utilization of water resources in China.

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.

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