Integrating Prediction of Precipitation and Hydrology for Early Actions: The InPRHA Project within the World Weather Research Programme
Bibliographic record
Abstract
Abstract Despite advancements in science and technology, flood prediction and preparedness remain challenging due to uncertainties in forecasting atmospheric and hydrologic processes, limited real-time data, and communication barriers. The Integrating Prediction of Precipitation and Hydrology for Early Actions (InPRHA) project, a 5-yr initiative under the WMO’s World Weather Research Programme, is the first to bring together meteorology, hydrology, and social sciences within a steering committee to address these challenges. Building on knowledge from the High Impact Weather (HiWeather) project, InPRHA focuses on multihazard flood forecasting across the entire warning value chain from minutes to days, in a rapidly changing world. A key emphasis is understanding flood predictability and how uncertainties cascade through forecasting systems and are perceived, communicated, and acted upon by diverse stakeholders. This includes bridging research and operations, examining socioeconomic, cultural, and environmental challenges that influence risk perception and response. We propose key scientific questions across seven themes that address critical gaps in integrating predictions along the flood warning value chain. Addressing these gaps requires collaboration across disciplines and agencies. The project is structured into four work packages: DEFINE (identifying challenges), CONSTRUCT (gathering case studies), EXPERIMENT (scientific evaluations), and ENGAGE (community collaboration). Research will span rural, urban, and underdeveloped regions as well as countries with established warning systems, ensuring broad applicability. We invite scientists and practitioners from meteorology, hydrology, hydraulics, impacts, communication, human behavior, and economics to collaborate. By integrating disciplines and fostering transdisciplinary research, InPRHA aims to advance the science and practice of flood forecasting and early warnings to better protect vulnerable communities at risk. Significance Statement InPHRA is a 5-yr project aimed at promoting international cooperation and advancing research to enhance flood hazard forecasting systems and warnings. By integrating precipitation and hydrologic predictions with social sciences, it seeks to improve early warning for communities in a rapidly changing world. InPRHA aims to reenvision the warning process by addressing flood multihazard interdependencies, local vulnerability, and climate change impacts on precipitation and hydrology forecasts. InPRHA calls on the broader research and operational community to collaborate on addressing key scientific questions and fostering transdisciplinary research across academia, research institutions, policymakers, and operational forecasting centers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.063 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".