Improvements in Forecasting Intense Rainfall: Results from the FRANC (Forecasting Rainfall Exploiting New Data Assimilation Techniques and Novel Observations of Convection) Project

Abstract

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall events.

A General Overview of the Risk-Reduction Strategies for Floods and Droughts

“…Satellites can observe soil and vegetation moisture or provide images to estimate the SPI to understand drought conditions [96][97][98]. A technique called data assimilation integrates remote sensing-based data and improves the performance of large-scale models for extreme forecasting, such as weather prediction models (WRFs) [99][100][101]. Along with artificial intelligence, satellite observations can then provide precipitation estimations [102].…”

Flash Flood!: a SeriousGeoGames activity combining science festivals, video games, and virtual reality with research data for communicating flood risk and geomorphology

“…The FFIR programme described itself as "A five year NERC funded programme aiming to reduce the risk of damage and loss of life caused by surface water and flash floods" (Flooding from Intense Rainfall, 2019). The programme, based in and focussed on the UK, brought to-gether experts from several universities, environmental consultancies, the Met Office, the Environment Agency, and the British Geological Survey to better understand the role intense and localised rainfall events had on both rural and urban flooding, with a strong focus on the end-to-end forecasting on events (Dance et al, 2019;Flack et al, 2019). Thunderstorms, driven by strong convection in summer months, form and dissipate rapidly and can be highly localised covering just a 1-3 km wide area.…”

“…During the threshold event and the river's recovery the amount of sediment delivered downstream in the system is greatly increased and this in turn may influence the flood risk in those areas (Lane et al, 2007;Slater, 2016). Predictions of climate change for the UK suggest flood events will become more likely and more extreme (Dankers and Feyen, 2008;Ekström et al, 2005;Feyen et al, 2012;Fowler and Ekström, 2009;Pall et al, 2011;Prudhomme et al, 2003) disrupting the balance determining river sensitivity; the impacts of this on rivers and future flood risk is not known but is likely to be negative and increase future flood risk.…”

Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales

“…This article resulted from the Natural Environment Research Council (NERC)/Met Office‐funded Flooding From Intense Rainfall programme, which was set up in 2013 to improve the United Kingdom's ability to predict flooding events (Clark et al ., ; Dance et al ., ). The key objective of this programme was to improve rainfall predictions by investigating new and improved ways of using radar observations, and possible improvements to the error covariance matrices in the DA scheme that initialises the Met Office regional model.…”

“…The importance of data assimilation (DA) has been appreciated for as long as numerical weather prediction (NWP) models have been used operationally (Daley, 1992), yet many obstacles related to the assimilation of atmospheric water (AW)-related observations remain. This is especially topical as there is a high (and growing) demand for observations of AW to assimilate with the latest high-resolution models, which are strongly affected by This article resulted from the Natural Environment Research Council (NERC)/Met Office-funded Flooding From Intense Rainfall programme, which was set up in 2013 to improve the United Kingdom's ability to predict flooding events (Clark et al, 2016;Dance et al, 2019).…”