Category Archives: data assimilation

Ensemble prediction: past, present and future

Contributed by Fredrik Wetterhall and Roberto Buizza, ECMWF The work of producing meteorological ensemble forecasts started 25 years ago at ECMWF and NCEP, and it sparked a revolution in both weather forecasts and its many applications. To celebrate this occasion, … Continue reading

Posted in activities, data assimilation, ensemble techniques, forecast techniques, forecast users, historical, meetings, operational systems, verification | 1 Comment

A never-ending struggle – Improving spring melt runoff forecast via snow information

Contributed by David Gustafsson (SMHI), member of the SMHI Guest Columnist Team As long as we can remember, the Swedish hydropower hydrologists have tried to improve the spring melt runoff predictions by integrating snow measurements in their forecast models. Various … Continue reading

Posted in case-studies, columnist, data assimilation, economic value, forecast users, seasonal prediction, Unclassified | 1 Comment

WhatsApp chat between a forecaster and a modeller in hydrology

Posted in data assimilation, decision making, hydrologic models, operational systems | 3 Comments

When does a model value become an observation?

Posted by @Hydrology_WSL (Massimiliano Zappa) on behalf also of @FPappenberger (Florian Pappenberger), @stagge_hydro (Jim Stagge) and @BteBrake (Bram te Brake). We acknowledge also tweets by @highlyanna (Anne Jefferson) and by @YoungHydrology. This blog post started out of the blue on … Continue reading

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Interactions between data assimilation and post-processing

I have recently contributed to a paper where we investigate how statistical post-processing and data assimilation (also called real-time model updating in the engineering community) can be intrinsically related in the hydrological forecasting framework. The paper, co-written with François Bourgin … Continue reading

Posted in data assimilation, postprocessing | 2 Comments

Data Assimilation for Operational Hydrology and Water Management

Contributed by Prof. DJ Seo, University of Texas at Arlington (Texas) Data assimilation is alive and well in HEPEX!   The 3rd International Workshop on Data Assimilation for Operational Hydrology and Water Management of the Hydrologic Ensemble Prediction Experiment (HEPEX-DAFOH III) was … Continue reading

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Joint workshop on CAHMDA-VI and HEPEX-DAFOH III

Contributed by Albrecht Weerts The sixth international conference on CAtchment-based Hydrological Model Data Assimilation (CAHMDA-VI) will be held jointly with the third International Workshop on Data Assimilation For Operational Hydrology and water management of the Hydrologic Ensemble Prediction Experiment (HEPEX-DAFOH … Continue reading

Posted in data assimilation, ensemble techniques, floods, forecast techniques, meetings, operational systems | Leave a comment

The Impact of using Ensemble Generator Produced Precipitation Estimates on the Calibration of a Hydrological Model

Contributed by Christopher J. Skinner The HEPEX Testbed Ensemble Representation of Rainfall Observation and Analysis Uncertainty was introduced in a previous blog post by Tim Bellerby. It aims to address some of the issues with representing uncertainty in precipitation observations … Continue reading

Posted in data assimilation, ensemble techniques, forecast techniques, testbed | Tagged | 2 Comments

Representing model error in high resolution ensemble forecasts

Contributed by Laura Baker Ensemble weather forecasts are used to represent the uncertainty in the forecast, rather than just giving a single deterministic forecast. In a very predictable system, all the ensemble members typically follow a similar path, while in … Continue reading

Posted in data assimilation, ensemble techniques, forecast techniques | Tagged | 1 Comment

Hydrologists make your voice heard (for atmospheric re-analysis)

ECMWF is currently conducting a user study regarding reanalysis data. The results will be used to shape future data provision and may well influence the design of future reanalysis data. IF you want reanalyses to stay (or be more) useful … Continue reading

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