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HS7.9/AS1.30/CL2.21/NH1.12/NP3.8

Precipitation variability: spatio-temporal scales and hydrometeorologic extremes (co-organized)PICO Session
Convener: Auguste Gires  | Co-Conveners: Alberto Viglione , Alexis Berne , Marco Borga , Alin Andrei Carsteanu , Salvatore Grimaldi , Demetris Koutsoyiannis , Andreas Langousis , Hidde Leijnse , Katharina Lengfeld , Sabine Lennartz-Sassinek , Remko Uijlenhoet , Venugopal Vuruputur , Xiaolan Wang 
PICO
 / Mon, 18 Apr, 08:30–10:00  / PICO spot 2
This session addresses through two sub-topics the issue of small scale precipitation variability and more generally the hydrometeorologic stochastics of extremes. It adopts a PICO format which aims at employing the most modern and captivating environment of scientific exchange (i.e., a 2-minute oral presentation, nicknamed "2-minute madness", followed by an interactive poster presentation on dedicated touch-screens, http://www.egu2016.eu/pico.html).

Precipitation variability: from drop scale to lot scale

The understanding of small scale spatio-temporal variability of precipitation from seconds in time and drop scale in space to 5 minutes in time and 1 km in space is essential for larger scale studies, including more and more hydrological applications, especially in highly heterogeneous areas (mountains, cities). Nevertheless grasping this variability remains an open challenge. An illustration of the range of scales involved is the ratio between the effective sampling areas of the commonly used point measurement devices (rain gauges and disdrometers) and weather radars, which is greater than 10^7! This session will bring together scientists and practitioners that aim at bridging this scale gap and improving the understanding of small scale precipitation variability, both liquid and solid, as well as its consequences at larger scales.
Contributions addressing one or several of the following issues are especially targeted:
- Novel measurement devices, combinations of devices (both in situ and remote sensors), or experimental set ups enabling to grasp small scale precipitation variability;
- Novel modelling or characterization tools of small scale precipitation variability relying on a wide range of approaches (e.g. scaling, (multi-)fractal, statistic, deterministic, numerical modelling);
- Precipitation drop (or particle) size distribution and its small scale variability, including its consequences for rain rate retrieval algorithms for radars and other remote sensors;
- Physical processes leading to the small-scale rainfall variability
- Examples of hydrological applications where small scale precipitation variability input is required.


Hydroclimatic and hydrometeorologic stochastics: Extremes, scales, probabilities

Over the last decades, a significant body of empirical and theoretical work has revealed the departure of statistical properties of hydrometeorological processes from the classical statistical prototype, as well as the scaling behaviour of their variables in general, and extremes in particular, in either state, space and/or time. This session welcomes the latest developments on:
- Coupling stochastic approaches with deterministic hydrometeorological predictions, in order to better represent predictive uncertainty;
- Stochastic-dynamic approaches that are more consistent with the hydrometeorological reality than both deterministic and statistical models separately;
- Variability at climatic scales and its interplay with the ergodicity of space-time probabilities;
- Linking underlying physics and scaling stochastics of hydrometeorological extremes;
- Development of parsimonious representations of probability distributions of hydrometeorological extremes over a wide range of scales and states;
- Understanding and using parsimonious parametrizations of extremes in risk analysis applications and hazard prediction.

The session is organized by the EGU/HS Subdivision on Precipitation and Climate and is cosponsored by the International Commission on Statistical Hydrology of International Association of Hydrological Sciences (ICSH-IAHS, former STAHY).