ECMWF | Reading | 25-28 November 2019
ECMWF is hosting the H SAF and HEPEX joint workshop on “Satellite inspired hydrology in an uncertain future”, with the following objectives:
- To review the status of the hydrological variables retrieval algorithms and to prepare future satellites (e.g. METEOSAT Third Generation, METOP‐SG, SWOT)
- To characterize the hydrological variable accuracy and to discuss requirements and validation metrics
- To review the status of modelling and data assimilation for hydrological and NWP applications and to share new ideas
- To strengthen links between the H SAF and the Risk management, Hydrological and NWP communities to promote a consistent Earth system approach.
The EUMETSAT Satellite Application Facility (SAF) for the support to operational hydrology and water management (H SAF) aims to provide new satellite‐derived products from existing and future satellites with sufficient time and space resolution to satisfy the needs of operational hydrology. H SAF focuses on operational satellite derived products of precipitation, snow and soil moisture as well as the continuous validation of these products.
The HEPEX (Hydrological Ensemble Prediction Experiment) is a community of researchers and practitioners for hydrologic ensemble prediction. It is a voluntarily initiative with many people contributing and working on specific topics related to hydrological forecasting and hydrometeorological ensemble prediction. HEPEX seeks to advance the science and practice of hydrologic ensemble prediction and its usage for risk-based decision making by engaging in several ongoing activities.
The workshop will consist of the 5 sessions listed below (including a non-exhaustive list of topics relevant to each session). The H SAF team will also present the H SAF soil moisture, snow and precipitation products at the beginning of the workshop.
The workshop will be followed by a demonstration session of the H SAF and HEPEX products on Thursday afternoon. All participants are welcome to attend.
Registration and abstract submission is now closed.
David Fairbairn, Patricia de Rosnay, Fredrik Wetterhall, Zuhal Akyurek, Rodolfo Alvarado, Luca Ciabatta, Simone Gabellani, Flavio Gattari, Ali Nadir Arslan, Silvia Puca, Paolo Sanò, Aynur Sensoy
1. Remote sensing, hydrological modelling and data assimilation
- Remote sensing techniques and products for hydrological variables, including soil moisture, precipitation and snow, especially for ensemble forecasting
- Reviewing current and planning for future satellite missions
- Assimilation and use of remotely sensed observations in land surface and hydrological models
2. Hydrological validation and benchmarking
- Validating products against ground measurements, satellite products or other hydrological models
- Evaluating a products performance against a suitable benchmark i.e. a model or data set with a defined expectation of performance
- Post-processing techniques for downscaling remote sensing to local applications
- Exploring novel validation techniques using spatial and temporal data
3. Hydrological data assimilation for NWP
- Assimilating remotely sensed and ground based observations into NWP models
- Validating the impact of hydrological assimilation on NWP forecast skill
- Coupled data assimilation approaches in NWP e.g. land-atmosphere
- Multi-sensor and ensemble data assimilation procedures
4. Impacts of hydrological uncertainty, hydrological forecasting and modelling
- Monitoring and forecasting of floods, droughts and other hydrological hazards
- Monitoring and modelling impacts of hydrological variability on water resources (e.g. rivers, reservoirs), including both natural (e.g. droughts) and anthropogenic (e.g. dams) influences;
- Incorporating uncertainty in hydrological forecasting (assessing uncertainties in initial conditions, parameter values, tendencies et cetera)
- Monitoring and modelling impacts of climate change on water resources
5. Novel hydrological data sources and assimilation techniques
- Novel data assimilation techniques, including coupled data assimilation approaches
- Integrating novel observational data in hydrological ensemble prediction systems (probabilistic-based inundation maps, crowdsourced data, citizen science approaches, etc.)
- New available data sources for hydrological data assimilation
- Linking large scale data and models to local knowledge and needs in hydrological forecasting and decision-making
- From traditional flood forecasting to impact-based forecasting (including e.g. inundation mapping, economic assessment, decision-based analysis.