On the links between sub-seasonal clustering of extreme precipitation and high discharge in Switzerland and Europe

River discharge is impacted by the sub-seasonal (weekly to monthly) temporal structure of precipitation. One example is the successive occurrence of extreme precipitation events over sub-seasonal timescales, referred to as temporal clustering. Its potential effects on discharge have received little attention. Here, we address this question by analysing discharge observations following extreme precipitation events either clustered in time or occurring in isolation. We rely on two sets of precipitation and discharge data, one centered on Switzerland and the other over Europe. We identify "clustered" extreme precipitation events based on the previous occurrence of another extreme precipitation within a given time window. We find that clustered events are generally followed by a more prolonged discharge response with a larger amplitude. The probability of exceeding the 95th discharge percentile in the five days following an extreme precipitation event is in particular up to twice as high for situations where another extreme precipitation event occurred in the preceding week compared to isolated extreme precipitation events. The influence of temporal clustering decreases as the clustering window increases; beyond 6–8 weeks the difference with non-clustered events is negligible. Catchment area, streamflow regime and precipitation magnitude also modulate the response. The impact of clustering is generally smaller in snow-dominated and large catchments. Additionally, particularly persistent periods of high discharge tend to occur in conjunction with temporal clusters of precipitation extremes.

Simulations in the Topography Effects of Tianshan Mountains on an Extreme Precipitation Event in the Ili River Valley, China

Xinjiang is located in an arid and semi-arid climate region in China, but Xinjiang Ili river valley is more humid, with higher precipitation intensity and precipitation, which is closely related to the role of the Tianshan Mountains. In this paper, through the NCRP 1° × 1° reanalysis data and the conventional observation data of the Ili River Valley in Xinjiang, the terrain sensitivity experiment conducted by the WRF model is used to analyze the short-term extreme precipitation event of the Ili River Valley from 18–19 of May 2017, to reveal the influence of Tianshan Mountains on the extreme precipitation event of the Ili River Valley. The results show that: (1) The reduction or removal of the terrain will cause a wide range of wind field changes, weaken the vertical upward movement of the windward slope, and the accumulation of water vapor before the windward slope will also be reduced; a large-scale change of the terrain will also affect the direction of water vapor transportation. These effects together lead to a decrease or increase in regional precipitation. (2) “Fuzzy” (smooth) terrain will affect the precipitation simulated by changing the local vertical movement and water vapor transport, which shows that the WRF model’s accurate description of the terrain structure characteristics of mountainous areas is beneficial to accurately simulate the precipitation process on the windward slope area.

Large-Scale Influences on Atmospheric River–Induced Extreme Precipitation Events along the Coast of Washington State

Transient, narrow plumes of strong water vapor transport, referred to as atmospheric rivers (ARs), are responsible for much of the precipitation along the West Coast of the United States. The most intense precipitation events are almost always induced by an AR on the coast of Oregon and Washington and can result in detrimental impacts on society due to mudslides and flooding. To accurately predict AR events on numerical weather prediction, subseasonal, and seasonal time scales, it is important to understand the large-scale impacts on extreme AR events. Here, characteristics of ARs that result in an extreme precipitation event are compared to typical ARs on the coast of Washington State. In addition to more intense water vapor transport, notable differences in the synoptic forcing are present during extreme precipitation events that are not present during typical AR events. Subseasonal and seasonal teleconnection patterns are known to influence the weather in the Pacific Northwest and are investigated here. The Madden–Julian oscillation (MJO) plays a role in determining the strength of precipitation associated with an AR on the Washington coast. Phase 5 of the MJO (convection centered over the Maritime Continent) is the most common phase during an extreme precipitation event, while phase 2 (convection over the Indian Ocean) discourages an extreme event from occurring. Interactions between El Niño–Southern Oscillation (ENSO) and the propagation speed of the MJO result in extreme events during phase 1 of the MJO and El Niño but phase 8 during neutral ESNO conditions.

Synoptic Characteristics of 14-Day Extreme Precipitation Events across the United States

Although significant improvements have been made to the prediction and understanding of extreme precipitation events in recent decades, there is still much to learn about these impactful events on the subseasonal time scale. This study focuses on identifying synoptic patterns and precursors ahead of an extreme precipitation event over the contiguous United States (CONUS). First, we provide a robust definition for 14-day “extreme precipitation events” and partition the CONUS into six different geographic regions to compare and contrast the synoptic patterns associated with events in those regions. Then, several atmospheric variables from ERA-Interim (e.g., geopotential height and zonal winds) are composited to understand the evolution of the atmospheric state before and during a 14-day extreme precipitation event. Common synoptic signals seen during events include significant zonally oriented trough–ridge patterns, an energized subtropical jet stream, and enhanced moisture transport into the affected area. Also, atmospheric-river activity increases in the specific region during these events. Modes of climate variability and lagged composites are then investigated for their potential use in lead-time prediction. Key findings include synoptic-scale anomalies in the North Pacific Ocean and regional connections to modes such as the Pacific–North American pattern and the North Pacific Oscillation. Taken together, our results represent a significant step forward in understanding the evolution of 14-day extreme precipitation events for potential damage and casualty mitigation.

Occurrence and Development of an Extreme Precipitation Event in the Ili Valley, Xinjiang, China and Analysis of Gravity Waves

We used observational data and the results from a high-resolution numerical simulation model to analyze the occurrence and development of an extreme precipitation event in the Ili Valley, Xinjiang, China on 26 June 2015. We analyzed the horizontal wavelength, period, speed, ducting, energy propagation and feedback mechanism of inertial gravity waves. A low-level convergence line was formed in the valley by the northerly and westerly winds as a result of Central Asian vortices and the trumpet-shaped topography of the Ili Valley. There was sufficient water vapor in the valley for the precipitation event to develop. A mesoscale vortex formed and developed on the low-level convergence line and the rainfall was distributed either near the convergence line or the mesoscale vortex. The low-level convergence line and the uplift caused by the terrain triggered convection, and then the convection triggered waves at lower levels. The combination of ascending motion induced by the lower level waves and the mesoscale vortex led to the development of convection, causing the precipitation to intensify. When the convection moved eastward to Gongliu County, it was coupled with the ascending phase of upper level waves, causing both the convection and precipitation to intensify again. We applied spectral analysis methods to verify that the waves were inertial gravity waves. The upper level inertial gravity waves propagated westward at a mean speed of −12 m s−1 with periods of 73–179 min and horizontal wavelengths of 50–55 km. The lower level inertial gravity waves propagated eastward at a mean speed of 8 m s−1 with periods of 73–200 min and a horizontal wavelength of 85 km. The more (less) favorable waveguide conditions determined whether the gravity waves persisted for a long (short) time and propagated for a longer (shorter) distance. Based on the mesoscale Eliassen–Palm flux theory, the wave energy of inertial gravity waves had an important effect on the maintenance and development of convection and precipitation by affecting wind strength and wind divergence. Feedback was mainly through the meridional and vertical transport of zonal momentum and the meridional transport of heat.

Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China

<p>Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE). Utilizing the C-band polarimetric (CPOL) radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (<em>Z</em><sub>H</sub>), the specific attenuation (<em>A</em><sub>H</sub>), the specific differential phase (<em>K</em><sub>DP</sub>), and their corresponding double-parameters that further integrate the differential reflectivity (<em>Z</em><sub>DR</sub>), namely <em>R</em>(<em>Z</em><sub>H</sub>, <em>Z</em><sub>DR</sub>), <em>R</em>(<em>K</em><sub>DP</sub>, <em>Z</em><sub>DR</sub>) and <em>R</em>(<em>A</em><sub>H</sub>, <em>Z</em><sub>DR</sub>), are investigated for an extreme precipitation event occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution (DSD) data observed by two disdrometers. The results show that (i) Each radar QPE estimator has its own advantages and disadvantages depending on the specific rainfall patterns, and it can outperform other estimators at a certain period of time; (ii) although <em>R</em>(<em>A</em><sub>H</sub>, <em>Z</em><sub>DR</sub>) underestimates in the light rain pattern, it performs best of all radar QPE estimators according to statistical scores; (iii) Both the optimal radar rainfall relationship and the consistency between radar measurements aloft and surface observations are required to obtain accurate rainfall estimates close to the ground. The contamination of melting solid hydrometeors on <em>A</em><sub>H</sub> and/or <em>K</em><sub>DP </sub>may make them less effective than <em>Z</em><sub>H</sub>. In addition, appropriate α coefficient can eliminate the melting impact on the <em>A</em><sub>H</sub>-based rainfall estimator.</p>

Climate Change’s Influence on June 2009 Extreme Precipitation Event Over Southeast Austria

<p>During 22-24 June 2009, Austria witnessed a rampant rainfall spell that spread across populated areas of the country. High-intensity rainfall caused 3000+ landslides in Feldbach, and property damages worth €10,000,000 in Styria itself. Numerous synoptic-scale studies indicated the presence of a cut-off low over the Adriatic and excessive moisture convergence behind the extreme event. In a warmer climate change scenario, such an extreme precipitation event may become more intense due to higher water holding capacity of air with increased temperatures, but this reasoning may not be so straightforward considering the complex physics of precipitation.</p><p>Precipitation, as a natural atmospheric phenomenon, is dependent upon the dynamic and thermodynamic characteristics of the atmosphere. While it is safe to say that the thermodynamic characteristics of the atmosphere are relatively easier to simulate with confidence using available global models, the same cannot be said about the dynamics. This can be blamed on the chaotic non-linear behaviour of the atmosphere and problem in resolving sub-grid scale processes that reduce the model accuracy for longer spatial scales.</p><p>CCLM regional model is used to study this extreme precipitation event. Our setup uses IFS data to calculate initial and boundary conditions for the simulations of the ‘present’ case where our attempt is to recreate the event over the same location as the original event. Further we use CMIP5 global climate models (at the RCP8.5) scenario. In particular, these will be applied in the ‘surrogate climate change’ method. Here, the climate change signals are calculated by computing the difference between the thermodynamic fields of the CMIP5 simulations for the future and the past. These climate change signals are applied to the original fields to obtain the ‘changed’ fields which are used to calculate new initial and boundary conditions resembling a climate-change future. A similar approach is to be applied for the ‘past’ case simulations.</p><p>The idea behind this experimental setup is to establish a ‘storyline’ for the event as it would have occurred in the past, present and the future. The storyline approach provides an alternative to the traditional probabilistic approach for assessing risk enhancement and can serve to study responses of different mechanisms to climate change. The storyline approach also helps in decision-making as event-oriented risk management is easy for people to perceive and respond to. An associated landslide modelling study, which uses the precipitation output of our simulations as input, looks into the probable increased risks of landslides in the region and will directly aid the lives of those living in Southeast Austria.</p>

Spatial and vertical structure of precipitating clouds and the role of background dynamics during extreme precipitation event as observed by C-band Polarimetric Doppler Weather Radar at Thumba (8.50° N, 77.00° E)

Extreme precipitation events have been cynosure for many meteorologists as well as for common men as it causes severe weather hazards and affects the densely populated regions, especially urban cities. It is now well known that these extreme events have been increasing over the Indian region during the past few years. It becomes very important to understand and assess these events, which is challenging in terms of limited observations. Very recently, the state of Kerala, India experienced extreme rainfall events during August 2018 and led to major flooding, which is regarded as one of the worst natural disasters experienced by Kerala in the last hundred years. This catastrophic event occurred during 12th to 17th August 2018 in which the Kerala state has received 60 % more rainfall than the normal during this period. The present study focuses on investigating the spatial and vertical structure of precipitating clouds and their microphysical properties during this extreme precipitation event using C-band Polarimetric Doppler Weather Radar (DWR) observations over Thumba (8.50° N, 77.00° E). The DWR analyses were carried out during episodes of extreme rainfall, and the time evolution of radar reflectivity structure is examined very closely to understand the structure and dynamics of this unprecedented event. The spatial and vertical structures of precipitating clouds are strongly linked with the background dynamics. Apart from the DWR observations, prevailing dynamics such as tropical easterly jet (TEJ), low-level jet (LLJ) along with vertical velocity also investigated, which showed distant signatures lead to the extreme event. It was observed that the upper level divergence existed associated with low level convergence, which aids to the development of convection. The westward equatorial waves were present in the period of 7–10 days throughout the month of August 2018. The weakening of TEJ at upper troposphere resulted in decrease of vertical shear, which favours the vertical growth of convective clouds leading to the extreme precipitation. The enhanced strength of LLJ is also contributed to the precipitation extreme. Thus, the significance of the present study lies in delineating the structure and dynamics of the extreme precipitation event using indigenously developed DWR.