scholarly journals Spatio-temporal patterns and extremity assessment of heavy rainfall events in Germany, derived from a radar-based catalogue (CatRaRE, 2001-2020)

2021 ◽  
Author(s):  
Ewelina Walawender ◽  
Katharina Lengfeld ◽  
Tanja Winterrath ◽  
Elmar Weigl ◽  
Andreas Becker
Keyword(s):  
Heavy Rainfall ◽  
Open Data ◽  
Heavy Precipitation ◽  
Radar Data ◽  
Rainfall Events ◽  
Antecedent Precipitation ◽  
Hourly Data ◽  
Spatio Temporal ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>One of the predicted effects of climate change in Central Europe is a growing number and increasing extremity of heavy rainfalls. Thus, it is of a great importance not only to develop best possible nowcasting methods and long-term forecasting models, but also to look closer at the structure and detailed characteristics of extreme events that have already taken place.</p><p>With this objective, the German Weather Service (DWD) has developed a Catalogue of Radar-based Heavy Rainfall Events (CatRaRE), derived from 20 years of climatological radar data for the area of Germany.</p><p>Using hourly data of about 1 km spatial resolution, an object-oriented analysis is performed to classify spatially and timely independent rainfall events exceeding the official warning level for heavy precipitation. Events with duration between 1 and 72 hours are investigated and statistically analysed. Apart from various extremity attributes, like return period, heavy precipitation, and weather extremity indices, the catalogue is enriched with additional variables (e.g. weather type, antecedent precipitation index, population density, land cover, imperviousness degree, Topographic Position Index), providing the meteorological background and helping to estimate the possible impact, each event could provoke.</p><p>The Catalogue is freely available via DWD’s Open Data Portal in both a tabular and spatial (GIS) format. In addition, a user friendly online Dashboard was developed to visualize the data and communicate our results to a broader audience. </p><p>We will present the CatRaRE Catalogue and results of a comprehensive analysis of all classified heavy precipitation events that occurred in Germany between 2001 and 2020. Different time scales from diurnal to multi-annual, as well as identified spatial patterns in connection with event attributes will be illustrated. Most common weather types, favouring occurrence of detected events will be outlined. Finally, we will demonstrate selected application possibilities by combining the catalogue with other datasets (e.g. fire brigade operations).</p>

scholarly journals Anomalous Weather Patterns in Relation to Heavy Precipitation Events in Japan during the Baiu Season

2015 ◽  
Vol 16 (2) ◽  
pp. 688-701 ◽  
Author(s):  
Masamichi Ohba ◽  
Shinji Kadokura ◽  
Yoshikatsu Yoshida ◽  
Daisuke Nohara ◽  
Yasushi Toyoda
Keyword(s):  
Time Scales ◽  
Heavy Rainfall ◽  
Southern Oscillation ◽  
Potential Temperature ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Weather Patterns ◽  
Extreme Precipitation Events ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract Anomalous weather patterns (WPs) in relation to heavy precipitation events during the baiu season in Japan are investigated using a nonlinear classification technique known as the self-organizing map (SOM). The analysis is performed on daily time scales using the Japanese 55-year Reanalysis Project (JRA-55) to determine the role of circulation and atmospheric moisture on extreme events and to investigate interannual and interdecadal variations for possible linkages with global-scale climate variability. SOM is simultaneously employed on four atmospheric variables over East Asia that are related to baiu front variability, whereby anomalous WPs that dominated during the 1958–2011 period are obtained. Our analysis extracts seven typical WPs, which are linked to frequent occurrences of heavy precipitation events. Each WP is associated with regional variations in the probability of extreme precipitation events. On interannual time scales, El Niño–Southern Oscillation (ENSO) affects the frequency of the WPs in relation to the heavy rainfall events. The warm phase of ENSO results in an increased frequency of a WP that provides a southwesterly intrusion of high equivalent potential temperature at low levels, while the cold phase provides southeastern intrusion. In addition, the results of this analysis suggest that interdecadal variability of frequency for heavy rainfall events corresponds to changes in frequency distributions of WPs and are not due to one particular WP.

Atmospheric Rivers in Association with Summer Heavy Rainfall over the Yangtze Plain on Sub-seasonal Time Scale

2021 ◽  
Author(s):  
Ping Liang ◽  
Guangtao Dong ◽  
Huqiang Zhang ◽  
Mei Zhao ◽  
Yue Ma
Keyword(s):  
Time Scales ◽  
Lead Time ◽  
Heavy Rainfall ◽  
East Asian ◽  
Heavy Precipitation ◽  
Rainfall Events ◽  
Atmospheric Rivers ◽  
The Tropics ◽  
Heavy Rainfall Events ◽  
Precipitation Events

<p>Atmospheric Rivers (ARs), referring to long and narrow bands of enhanced water vapor transport, mainly from the tropics into the mid-latitudes in the low atmosphere. They often contribute to heavy rainfall generations outside the tropics. However, there is a lack of such AR studies in East Asia and it is still unclear how ARs act on different time scales during the boreal summer when frequent heavy precipitation events take place over the region. In this study, climatological ARs and their evolutions on both synoptic and sub-seasonal time scales associated with heavy rainfall events over the Yangtze Plain in China are investigated. Furthermore, its predictability is assessed by examining hindcast skills from an operational coupled seasonal forecast model. Results show that ARs embedded within the South Asian monsoon and Somali cross-equatorial flow provide a favorable background for steady moisture supply of summer rainfall into East Asia. We can call this favorable background as a climatological East Asian AR which has close connections with seasonal cycle and climatological intra-seasonal oscillation (CISO) of rainfall in the Yangtze Plain during its Meiyu season. The East Asian AR is also influenced by anomalous anti-cyclonic circulations over the tropical West Pacific when heavy rainfall events occur over the Yangtze Plain. Different from orography-induced precipitation, ARs leading to heavy rainfall over the Yangtze Plain are linked with the intrusions of cold air from its north. The major source of ARs responsible for heavy precipitation events over the Yangtze Plain appears to originate from tropical West Pacific on both synoptic and sub-seasonal time scales. By analyzing 23-yr hindcasts for May-June-July with start date of 1 May, we show that the current operational coupled seasonal forecast system of the Australian Bureau of Meteorology (named as ACCESS-S1) has skillful rainfall forecasts at lead-time of 0 month (i.e. forecasting May monthly mean with initial conditions on 1 May), but the skill degrades significantly at longer lead time. Nevertheless, the model shows skills in predicting the variations of low-level moisture transport affecting the Yangtze River at longer lead time, suggesting that the ARs influencing summer monsoon rainfall in the East Asian region are likely to be more predictable than rainfall itself. This provides a potential of utilizing the skill from the coupled forecast system in predicting ARs to guide its rainfall forecasts in the East Asian summer season at longer lead time.</p>

scholarly journals Evaluating the extreme precipitation events using a mesoscale atmosphere model and satellite based precipitation product

2013 ◽  
Vol 1 (6) ◽  
pp. 6979-7014
Author(s):  
I. Yucel ◽  
A. Onen
Keyword(s):  
Data Assimilation ◽  
Extreme Precipitation ◽  
Heavy Rainfall ◽  
Wrf Model ◽  
Hydrologic Model ◽  
Rainfall Events ◽  
Extreme Precipitation Events ◽  
Precipitation Estimates ◽  
Heavy Rainfall Events ◽  
Precipitation Events

Abstract. Quantitative precipitation estimates are obtained with more uncertainty under the influence of changing climate variability and complex topography from numerical weather prediction (NWP) models. On the other hand, hydrologic model simulations depend heavily on the availability of reliable precipitation estimates. Difficulties in estimating precipitation impose an important limitation on the possibility and reliability of hydrologic forecasting and early warning systems. This study examines the performance of the Weather Research and Forecasting (WRF) model and the Multi Precipitation Estimates (MPE) algorithm in producing the temporal and spatial characteristics of the number of extreme precipitation events observed in the West Black Sea Region of Turkey. Precipitations derived from WRF model with and without three-dimensional variational (3-DVAR) data assimilation scheme and MPE algorithm at high spatial resolution (4 km) are compared with gauge precipitation. WRF-derived precipitation showed capabilities in capturing the timing of precipitation extremes and in some extent the spatial distribution and magnitude of the heavy rainfall events wheras MPE showed relatively weak skills in these aspects. WRF skills in estimating such precipitation characteristics are enhanced with the application of 3-DVAR scheme. Direct impact of data assimilation on WRF precipitation reached to 12% and at some points there exists quantitative match for heavy rainfall events, which are critical for hydrological forecast.

Origins of Heavy Precipitation Biases in the TRMM PR and TMI Products Assessed with CloudSat and Reanalysis Data

2019 ◽  
Vol 58 (1) ◽  
pp. 37-54 ◽  
Author(s):  
Andung Bayu Sekaranom ◽  
Hirohiko Masunaga
Keyword(s):  
Heavy Rainfall ◽  
Tropical Rainfall Measuring Mission ◽  
Heavy Precipitation ◽  
Reanalysis Data ◽  
Observation Data ◽  
Tropical Ocean ◽  
Rainfall Events ◽  
Convective Systems ◽  
Microwave Imager ◽  
Heavy Rainfall Events

AbstractThis study aims to characterize the background physical processes in the development of those heavy precipitation clouds that contribute to the Tropical Rainfall Measuring Mission (TRMM) active and passive sensor differences. The combined global observation data from TRMM, CloudSat, and European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) from 2006 to 2014 were utilized to address this issue. Heavy rainfall events were extracted from the top 10% of the rain events from the Precipitation Radar (PR) and TRMM Microwave Imager (TMI) rain-rate climatology. Composite analyses of CloudSat and ERA-Interim were conducted to identify the detailed cloud structures and the background environmental conditions. Over tropical land, TMI tends to preferentially detect deep isolated precipitation clouds for relatively drier and unstable environments, while PR identifies more organized systems. Over the tropical ocean, TMI identifies heavy rainfall events with notable convective organization and clear regional gradients between the western and eastern Pacific Ocean, while PR fails to capture the eastward shallowing of convective systems. The PR–TMI differences for the moist and stable environments are reversed over tropical land.

scholarly journals Objective Evaluation of Satellite Precipitation Datasets for Heavy Precipitation Events Caused by Typhoons in the Philippines

2021 ◽  
Author(s):  
Putu Aryastana ◽  
Chian-Yi Liu ◽  
Ben Jong-Dao Jou ◽  
Esperanza Cayanan ◽  
Jason Pajimola Punay
Keyword(s):  
Heavy Rainfall ◽  
Heavy Precipitation ◽  
The Philippines ◽  
Superior Performance ◽  
Rainfall Events ◽  
Satellite Precipitation ◽  
Remote Areas ◽  
Wind Speeds ◽  
Precipitation Events ◽  
Strong Ability

Abstract Extreme weather events, such as typhoons, have occurred more frequently in the last few decades in the Philippines. The heavy precipitation caused by typhoons is difficult to measure with traditional instruments, such as rain gauges and ground-based radar, because these instruments have an uneven distribution in remote areas. Satellite precipitation datasets (SPDs) provide integrated spatial coverage of rainfall measurements, even for remote areas. This study performed subdaily (3-hour) assessments of SPDs (i.e., the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement [IMERG], Global Satellite Mapping of Precipitation [GSMaP], and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks datasets) during five typhoon-related heavy precipitation events in the Philippines between 2016 and 2018. The aforementioned assessments were performed through a point-to-grid comparison by using continuous and volumetric statistical validation indices for the 34-knot wind radii of the typhoons, rainfall intensity, the terrain, and wind velocity effects. The results revealed that the IMERG exhibited good agreement with rain gauge measurements and exhibited high performance in detecting rainfall during five typhoon events, whereas the GSMaP exhibited high agreement during peak rainfall. All the SPDs tended to overestimate rainfall during light to moderate rainfall events and underestimate rainfall during heavy to extreme events. The IMERG exhibited a strong ability to detect moderate rainfall events (5–15 mm/3 hours), whereas the GSMaP exhibited superior performance in detecting heavy to extreme rainfall events (15–25, 25–50, and >50 mm/3 hours). The GSMaP exhibited the best performance for detecting heavy rainfall at high elevations, whereas the IMERG exhibited the best performance for rainfall detection at low elevations. The IMERG exhibited a strong ability to detect heavy rainfall under various wind speeds. A strong ability to detect heavy rainfall events for different wind speeds in the western and eastern parts of the mountainous region of Luzon were found for the GSMap and IMERG, respectively. This study demonstrated that the IMERG and GSMaP datasets exhibit promising performance in detecting heavy precipitation caused by typhoon events.

scholarly journals A Comparison of Two Heavy Rainfall Events during the Terrain-Influenced Monsoon Rainfall Experiment (TiMREX) 2008

2014 ◽  
Vol 142 (7) ◽  
pp. 2436-2463 ◽  
Author(s):  
Chuan-Chi Tu ◽  
Yi-Leng Chen ◽  
Ching-Sen Chen ◽  
Pay-Liam Lin ◽  
Po-Hsiung Lin
Keyword(s):  
Rainy Season ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Sea Breeze ◽  
Heavy Precipitation ◽  
Early Summer ◽  
Cold Pool ◽  
Mountain Range ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract Two contrasting localized heavy rainfall events during Taiwan’s early summer rainy season with the daily rainfall maximum along the windward mountain range and coast were studied and compared using a combination of observations and numerical simulations. Both events occurred under favorable large-scale settings including the existence of a moisture tongue from the tropics. For the 31 May case, heavy rainfall occurred in the afternoon hours over the southwestern windward slopes after a shallow surface front passed central Taiwan. The orographic lifting of the prevailing warm, moist, west-southwesterly flow aloft, combined with a sea breeze–upslope flow at the surface provided the localized lifting needed for the development of heavy precipitation. On 16 June before sunrise, pronounced orographic blocking of the warm, moist, south-southwesterly flow occurred because of the presence of relatively cold air at low levels as a result of nocturnal and rain evaporative cooling. As a result, convective systems intensified as they moved toward the southwestern coast. During the daytime, the cold pool remained over southwestern Taiwan without the development of onshore/upslope flow. Furthermore, with a south-southwesterly flow aloft parallel to terrain contours, orographic lifting aloft was absent and preexisting rain cells offshore diminished after they moved inland. Over northern Taiwan on the lee side, a sea breeze/onshore flow developed in the afternoon hours, resulting in heavy thundershowers. These results demonstrate the importance of diurnal and local effects on determining the location and timing of the occurrences of localized heavy precipitation during the early summer rainy season over Taiwan.

scholarly journals Spatial organization of connectivity in functional climate networks describing event synchrony of heavy precipitation

2021 ◽  
Author(s):  
Frederik Wolf ◽  
Reik V. Donner
Keyword(s):  
Heavy Rainfall ◽  
Spatial Organization ◽  
Clustering Coefficient ◽  
Temporal Organization ◽  
Rainfall Events ◽  
Link Density ◽  
Network Properties ◽  
Spatio Temporal ◽  
Heavy Rainfall Events ◽  
Climate Networks

AbstractIn the past years, there has been an increasing number of applications of functional climate networks to studying the spatio-temporal organization of heavy rainfall events or similar types of extreme behavior in some climate variable of interest. Nearly all existing studies have employed the concept of event synchronization (ES) to statistically measure similarity in the timing of events at different grid points. Recently, it has been pointed out that this measure can however lead to biases in the presence of events that are heavily clustered in time. Here, we present an analysis of the effects of event declustering on the resulting functional climate network properties describing spatio-temporal patterns of heavy rainfall events during the South American monsoon season based on ES and a conceptually similar method, event coincidence analysis (ECA). As examples for widely employed local (per-node) network characteristics of different type, we study the degree, local clustering coefficient and average link distance patterns, as well as their mutual interdependency, for three different values of the link density. Our results demonstrate that the link density can markedly affect the resulting spatial patterns. Specifically, we find the qualitative inversion of the degree pattern with rising link density in one of the studied settings. To our best knowledge, such crossover behavior has not been described before in event synchrony based networks. In addition, declustering relieves differences between ES and ECA based network properties in some measures while not in others. This underlines the need for a careful choice of the methodological settings in functional climate network studies of extreme events and associated interpretation of the obtained results, especially when higher-order network properties are considered.

scholarly journals Linking ENSO and heavy rainfall events over coastal British Columbia through a weather pattern classification

2013 ◽  
Vol 17 (4) ◽  
pp. 1455-1473 ◽  
Author(s):  
P. Brigode ◽  
Z. Mićović ◽  
P. Bernardara ◽  
E. Paquet ◽  
F. Garavaglia ◽  
...  
Keyword(s):  
British Columbia ◽  
Heavy Rainfall ◽  
Weather Forecasting ◽  
Coastal Region ◽  
Circulation Model ◽  
Heavy Precipitation ◽  
Global Circulation Model ◽  
Bottom Up ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Classifications of atmospheric weather patterns (WPs) are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia (BC) coastal region (Canada) and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March), which is the period of heavy precipitation events in coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples (i.e. sub-samples constituted by days having similar atmospheric circulation patterns) and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño-Southern Oscillations (ENSO) significantly influence the frequency of occurrence of two coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, heavy rainfall estimations do not show significant evolution of heavy rainfall behaviour between Niño and Niña winters. However, the WP approach captures the variability of the probability of occurrences of synoptic situations generating heavy rainfall depending on ENSO and opening interesting perspectives for the analysis of heavy rainfall distribution in a non-stationary context.

scholarly journals Linking ENSO and heavy rainfall events over Coastal British Columbia through a weather pattern classification

2012 ◽  
Vol 9 (10) ◽  
pp. 11733-11768
Author(s):  
P. Brigode ◽  
Z. Mićović ◽  
P. Bernardara ◽  
E. Paquet ◽  
F. Garavaglia ◽  
...  
Keyword(s):  
British Columbia ◽  
Heavy Rainfall ◽  
Weather Forecasting ◽  
Coastal Region ◽  
Circulation Model ◽  
Heavy Precipitation ◽  
Global Circulation Model ◽  
Bottom Up ◽  
Rainfall Events ◽  
Heavy Rainfall Events

Abstract. Classifications of atmospheric weather patterns (WPs) are widely used for the description of the climate of a given region and are employed for many applications, such as weather forecasting, downscaling of global circulation model outputs and reconstruction of past climates. WP classifications were recently used to improve the statistical characterisation of heavy rainfall. In this context, bottom-up approaches, combining spatial distribution of heavy rainfall observations and geopotential height fields have been used to define WP classifications relevant for heavy rainfall statistical analysis. The definition of WPs at the synoptic scale creates an interesting variable which could be used as a link between the global scale of climate signals and the local scale of precipitation station measurements. We introduce here a new WP classification centred on the British Columbia Coastal region (Canada) and based on a bottom-up approach. Five contrasted WPs composed this classification, four rainy WPs and one non-rainy WP, the anticyclonic pattern. The four rainy WPs are mainly observed in the winter months (October to March), which is the period of heavy precipitation events in Coastal BC and is thus consistent with the local climatology. The combination of this WP classification with the seasonal description of rainfall is shown to be useful for splitting observed precipitation series into more homogeneous sub-samples and thus identifying, for each station, the synoptic situations that generate the highest hazard in terms of heavy rainfall events. El Niño Southern Oscillations significantly influence the frequency of occurrence of two Coastal BC WPs. Within each WP, ENSO seem to influence only the frequency of rainy events and not the magnitudes of heavy rainfall events. Consequently, MEWP heavy rainfall estimations do not show significant evolution of heavy rainfall behaviour between Niño and Niña winters. However, the WP approach captures the variability of the probability of occurrences of synoptic situations generating heavy rainfall depending on ENSO and opening interesting perspectives for the analysis of heavy rainfall distribution in a non-stationary context.

scholarly journals Extreme Floods in the Eastern Part of Europe: Large-Scale Drivers and Associated Impacts

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1122
Author(s):  
Monica Ionita ◽  
Viorica Nagavciuc
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Large Scale ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Flood Events ◽  
Rainfall Events ◽  
Catchment Areas ◽  
Large Scale Atmospheric Circulation ◽  
Heavy Rainfall Events

The role of the large-scale atmospheric circulation in producing heavy rainfall events and floods in the eastern part of Europe, with a special focus on the Siret and Prut catchment areas (Romania), is analyzed in this study. Moreover, a detailed analysis of the socio-economic impacts of the most extreme flood events (e.g., July 2008, June–July 2010, and June 2020) is given. Analysis of the largest flood events indicates that the flood peaks have been preceded up to 6 days in advance by intrusions of high Potential Vorticity (PV) anomalies toward the southeastern part of Europe, persistent cut-off lows over the analyzed region, and increased water vapor transport over the catchment areas of Siret and Prut Rivers. The vertically integrated water vapor transport prior to the flood peak exceeds 300 kg m−1 s−1, leading to heavy rainfall events. We also show that the implementation of the Flood Management Plan in Romania had positive results during the 2020 flood event compared with the other flood events, when the authorities took several precaution measurements that mitigated in a better way the socio-economic impact and risks of the flood event. The results presented in this study offer new insights regarding the importance of large-scale atmospheric circulation and water vapor transport as drivers of extreme flooding in the eastern part of Europe and could lead to a better flood forecast and flood risk management.

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