"Precipitation modelling: uncertainty, variability, assimilation, ensemble simulation and downscaling" (HS7.1/CL5.3.20/NH1.12/NP3.8).

 Dear Colleagues,

 

I hope this email finds you well.

 

We would like to invite you to contribute to the EGU session: 

"Precipitation modelling: uncertainty, variability, assimilation, ensemble simulation and downscaling" (HS7.1/CL5.3.20/NH1.12/NP3.8).

 

We are pleased to announce that our invited speaker is Dr. Athanasios Paschalis from Imperial College London.

 

The deadline for abstract submission is 12 January 2022, 13:00 CET.

Abstracts can be submitted at this link.

 

Session details:

The assessment of precipitation variability and uncertainty is crucial in a variety of applications, such as flood risk forecasting, water resource assessments, evaluation of the hydrological impacts of climate change, determination of design floods, and hydrological modelling in general. This session aims to gather contributions on research, advanced applications, and future needs in the understanding and modelling of precipitation variability, and its sources of uncertainty.
Contributions focusing on one or more of the following issues are particularly welcome:
- Novel studies aimed at the assessment and representation of different sources of uncertainty versus natural variability of precipitation.
- Methods to account for accuracy in precipitation time series due to, e.g., change and improvement of observation networks.
- Uncertainty and variability in spatially and temporally heterogeneous multi-source precipitation products.
- Estimation of precipitation variability and uncertainty at ungauged sites.
- Precipitation data assimilation.
- Process conceptualization and approaches to modelling of precipitation at different spatial and temporal scales, including model parameter identification and calibration, and sensitivity analyses to parameterization and scales of process representation.
- Modelling approaches based on ensemble simulations and methods for synthetic representation of precipitation variability and uncertainty.
- Scaling and scale invariance properties of precipitation fields in space and/or in time.
- Physically and statistically based approaches to downscale information from meteorological and climate models to spatial and temporal scales useful for hydrological modelling and applications.

 

Apologies for cross-posting.

 

Kind regards,

 

Session Conveners:

Giuseppe Mascaro

Alin Andrei Carsteanu 

Simone Fatichi

Roberto Deidda 

Chris Onof