scholarly journals Improvements in subseasonal forecasts of rainfall extremes by statistical postprocessing methods

2021 ◽  
Vol 34 ◽  
pp. 100384
Author(s):  
Ming Li ◽  
Huidong Jin ◽  
Quanxi Shao
Keyword(s):  
Rainfall Extremes

Independent ENSO and IOD impacts on rainfall extremes over Indonesia

2021 ◽  
Vol 41 (6) ◽  
pp. 3640-3656
Author(s):  
Ari Kurniadi ◽  
Evan Weller ◽  
Seung‐Ki Min ◽  
Min‐Gyu Seong
Keyword(s):  
Rainfall Extremes

scholarly journals A quasi-global assessment of changes in remotely sensed rainfall extremes with temperature

2016 ◽  
Vol 43 (24) ◽  
pp. 12,659-12,668 ◽  
Author(s):  
C. Wasko ◽  
R. M. Parinussa ◽  
A. Sharma
Keyword(s):  
Global Assessment ◽  
Remotely Sensed ◽  
Rainfall Extremes

scholarly journals Long-term oscillations in rainfall extremes in a 268 year daily time series

2015 ◽  
Vol 51 (1) ◽  
pp. 639-647 ◽  
Author(s):  
Marco Marani ◽  
Stefano Zanetti
Keyword(s):  
Time Series ◽  
Rainfall Extremes ◽  
Daily Time Series ◽  
Daily Time

The effect of climate change on urban drainage: an evaluation based on regional climate model simulations

2006 ◽  
Vol 54 (6-7) ◽  
pp. 9-15 ◽  
Author(s):  
M. Grum ◽  
A.T. Jørgensen ◽  
R.M. Johansen ◽  
J.J. Linde
Keyword(s):  
Climate Change ◽  
Extreme Precipitation ◽  
Climate Model ◽  
Regional Climate ◽  
Rain Gauge ◽  
Urban Drainage ◽  
Extreme Precipitation Events ◽  
Rainfall Extremes ◽  
The Future ◽  
Precipitation Events

That we are in a period of extraordinary rates of climate change is today evident. These climate changes are likely to impact local weather conditions with direct impacts on precipitation patterns and urban drainage. In recent years several studies have focused on revealing the nature, extent and consequences of climate change on urban drainage and urban runoff pollution issues. This study uses predictions from a regional climate model to look at the effects of climate change on extreme precipitation events. Results are presented in terms of point rainfall extremes. The analysis involves three steps: Firstly, hourly rainfall intensities from 16 point rain gauges are averaged to create a rain gauge equivalent intensity for a 25 × 25 km square corresponding to one grid cell in the climate model. Secondly, the differences between present and future in the climate model is used to project the hourly extreme statistics of the rain gauge surface into the future. Thirdly, the future extremes of the square surface area are downscaled to give point rainfall extremes of the future. The results and conclusions rely heavily on the regional model's suitability in describing extremes at time-scales relevant to urban drainage. However, in spite of these uncertainties, and others raised in the discussion, the tendency is clear: extreme precipitation events effecting urban drainage and causing flooding will become more frequent as a result of climate change.

How landscape and climate affect the spatial variability of the Italian rainfall extremes? Some initial clues based on I2-RED

2021 ◽  
Author(s):  
Paola Mazzoglio ◽  
Ilaria Butera ◽  
Pierluigi Claps
Keyword(s):  
Spatial Variability ◽  
Linear Regression Analysis ◽  
Alpine Region ◽  
Annual Rainfall ◽  
Mean Values ◽  
Rainfall Extremes ◽  
Geomorphological Parameters ◽  
The Mean ◽  
The Relationship ◽  
Italian Territory

<p>The intensity and the spatial distribution of precipitation depths are known to be highly dependent on relief and geomorphological parameters. Complex environments like mountainous regions are prone to intense and frequent precipitation events, especially if located near the coastline. Although the link between the mean annual rainfall and geomorphological parameters has received substantial attention, few literature studies investigate the relationship between the sub-daily maximum annual rainfall depth and geographical or morphological landscape features.<br>In this study, the mean of the rainfall extremes in Italy, recently revised in the so-called I<sup>2</sup>-RED dataset, are investigated in their spatial variability in comparison with some landscape and also some broad climatic characteristics. The database includes all sub-daily rainfall extremes recorded in Italy from 1916 until 2019 and this analysis considers their mean values (from 1 to 24 hours) in stations with at least 10 years of records, involving more than 3700 stations.<br>The geo-morpho-climatic factors considered range from latitude, longitude and minimum distance from the coastline on the geographic side, to elevation, slope, openness and obstruction morphological indices, and also include an often-neglected robust climatological information, as the local mean annual rainfall.<br>Obtained results highlight that the relationship between the annual maximum rainfall depths and the hydro-geomorphological parameters is not univocal over the entire Italian territory and over different time intervals. Considering the whole of Italy, the highest correlation is reached between the mean values of the 24-hours records and the mean annual precipitation (correlation coefficient greater than 0.75). This predominance remains also in sub-areas of the Italian territory (i.e., the Alpine region, the Apennines or the coastal areas) but correlation decreases as the time interval decreases, except for the Alpine region (0.73 for the 1-hour maximum). The other geomorphological parameters seem to act in conjunction, making it difficult to evaluate, with a simple linear regression analysis, their impact. As an example, the absolute value of the correlation coefficient between the elevation and the 1-hour extremes is greater than 0.35 for the Italian and the Alpine regions, while for the 24-hours interval it is greater than 0.35 over the coastal areas.<br>To further investigate the spatial variability of the relationship between rainfall and elevation, a spatial linear regression analysis has been undertaken. Local linear relationships have been fitted in circles centered on any of the 0.5-km size pixels in Italy, with 1 to 30 km radius and at least 5 stations included. Results indicate the need of more comprehensive terrain analysis to better understand the causes of local increasing or decreasing relations, poorly described in the available literature.</p>

Listening to Stakeholders: Initiating Research on Sub-seasonal to Seasonal Heavy Precipitation Events in the Contiguous U.S. by First Understanding What Stakeholders Need

2021 ◽  
pp. 1-35
Author(s):  
Olivia VanBuskirk ◽  
Paulina Ćwik ◽  
Renee A. McPherson ◽  
Heather Lazrus ◽  
Elinor Martin ◽  
...  
Keyword(s):  
United States ◽  
Environmental Management ◽  
Predictive Models ◽  
Extreme Precipitation ◽  
Heavy Precipitation ◽  
Role Playing ◽  
Extreme Precipitation Events ◽  
Rainfall Extremes ◽  
Actionable Science ◽  
Precipitation Events

AbstractHeavy precipitation events and their associated flooding can have major impacts on communities and stakeholders. There is a lack of knowledge, however, about how stakeholders make decisions at the sub-seasonal to seasonal (S2S) timescales (i.e., two weeks to three months). To understand how decisions are made and S2S predictions are or can be used, the project team for “Prediction of Rainfall Extremes at Sub-seasonal to Seasonal Periods” (PRES2iP) conducted a two-day workshop in Norman, Oklahoma, during July 2018. The workshop engaged 21 professionals from environmental management and public safety communities across the contiguous United States in activities to understand their needs for S2S predictions of potential extended heavy precipitation events. Discussions and role-playing activities aimed to identify how workshop participants manage uncertainty and define extreme precipitation, the timescales over which they make key decisions, and the types of products they use currently. This collaboration with stakeholders has been an integral part of PRES2iP research and has aimed to foster actionable science. The PRES2iP team is using the information produced from this workshop to inform the development of predictive models for extended heavy precipitation events and to collaboratively design new forecast products with our stakeholders, empowering them to make more-informed decisions about potential extreme precipitation events.

scholarly journals Evidence for Increasing Rainfall Extremes Remains Elusive at Large Spatial Scales: The Case of Italy

2019 ◽  
Vol 46 (13) ◽  
pp. 7437-7446 ◽  
Author(s):  
A. Libertino ◽  
D. Ganora ◽  
P. Claps
Keyword(s):  
Spatial Scales ◽  
Rainfall Extremes

scholarly journals At-site and regional frequency analysis of extreme precipitation from radar-based estimates

2017 ◽  
Author(s):  
Edouard Goudenhoofdt ◽  
Laurent Delobbe ◽  
Patrick Willems
Keyword(s):  
Confidence Interval ◽  
Frequency Analysis ◽  
Extreme Precipitation ◽  
Rain Gauge ◽  
Regional Frequency Analysis ◽  
Extreme Value Analysis ◽  
Value Analysis ◽  
Rain Gauges ◽  
Rainfall Extremes ◽  
Regional Frequency

Abstract. In Belgium, only rain gauge time-series have been used so far to study extreme precipitation at a given location. In this paper, the potential of a 12-year quantitative precipitation estimation (QPE) from a single weather radar is evaluated. For the period 2005–2016, independent sliding 1 h and 24 h rainfall extremes from automatic rain gauges and collocated radar estimates are compared. The extremes are fitted to the exponential distribution using regression in QQ-plots with a threshold rank which minimises the mean squared error. A basic radar product used as reference exhibits unrealistic high extremes and is not suitable for extreme value analysis. For 24 h rainfall extremes, which occur partly in winter, the radar-based QPE needs a bias correction. A few missing events are caused by the wind drift of convective cells and strong radar signal attenuation. Differences between radar and gauge values are caused by spatial and temporal sampling, gauge rainfall underestimations and radar errors due to the relation between reflectivity and rain rate. Nonetheless the fit to the QPE data is within the confidence interval of the gauge fit, which remains large due to the short study period. A regional frequency analysis is performed on radar data within 20 km of the locations of 4 rain gauges with records from 1965 to 2008. Assuming that the extremes are correlated within the region, the fit to the two closest rain gauge data is within the confidence interval of the radar fit, which is small due to the sample size. In Brussels, the extremes on the period 1965–2008 from a rain gauge are significantly lower than the extremes from an automatic gauge and the radar on the period 2005–2016. For 1 h duration, the location parameter varies slightly with topography and the scale parameter exhibits some variations from region to region. The radar-based extreme value analysis can be extended to other durations.

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