Dear Colleagues,

Changes in the global climate amplify the risk of hydrometeorological hazards, such as rainstorms, hurricanes, floods, droughts, landslides, storm surges, and heat/cold waves. Accurate and timely prediction of extreme hydrometeorological events is key to the risk management of hydrometeorological hazards. Traditionally, the prediction is based on a single best guess from a calibrated numerical model, which is known as the deterministic forecast. It, however, provides little or no uncertainty information associated with model structure, model parameters, input data, and evaluation data. Over the past few decades, hydrometeorological prediction has gradually shifted from deterministic to probabilistic forecasting using ensemble prediction systems. Unlike deterministic forecast, ensemble forecast provides multiple guesses for the same events by perturbing uncertain factors such as initial conditions, forcing data, and model parameterizations/parameters, which would help decision makers with risk assessment of all possible outcomes. Hydrometeorological ensemble prediction has achieved considerable success in the last decade due to the development of meteorological/hydrological forecasting capabilities, availability of more field-measured and remotely sensed data, and improvements in computing capabilities. Nonetheless, substantial challenges still exist due to the growing complexity of ensemble prediction systems, requirement of timely and efficient handling of massive volumes of data, and increasing demands of computing resources.

This Special Issue calls for original research or review papers that are related to any aspect of hydrometeorological ensemble prediction. Potential topics include but are not limited to:

• Ensemble prediction of extreme hydrometeorological events;
• Experimental/operational ensemble forecasting systems and services for meteorologic/hydrologic forecasts;
• Utilization of observational data from ground-based stations, radars, or satellites for hydrometeorological prediction;
• Data assimilation, machine learning, and big data applications in hydrometeorology;
• Calibration, validation, and uncertainty analysis of meteorological/hydrological models;
• Evaluation of numerical weather prediction model products, or driven hydrology or water resources products;
• Post-processing of meteorological/hydrological (re-)forecasts.

Dr. Yanjun Gan
Dr. Haksu Lee
Dr. Hongjun Bao
Dr. Hongbin Zhang
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• extreme hydrometeorological events
• ensemble forecasting
• numerical weather prediction
• hydrological prediction
• data assimilation
• model calibration
• uncertainty analysis
• statistical post-processing