Introducing uncertainty of radar-rainfall estimates to the verification of mesoscale model precipitation forecasts

Abstract

Abstract.A simple measure of the uncertainty associated with using radar-derived rainfall estimates as "truth" has been introduced to the Numerical Weather Prediction (NWP) verification process to assess the effect on forecast skill and errors. Deterministic precipitation forecasts from the mesoscale version of the UK Met Office Unified Model for a two-day high-impact event and for a month were verified at the daily and six-hourly time scale using a spatially-based intensity-scale method and various traditional skill scores such as the Equitable Threat Score (ETS) and log-odds ratio. Radar-rainfall accumulations from the UK Nimrod radarcomposite were used.The results show that the inclusion of uncertainty has some effect, shifting the forecast errors and skill. The study also allowed for the comparison of results from the intensity-scale method and traditional skill scores. It showed that the two methods complement each other, one detailing the scale and rainfall accumulation thresholds where the errors occur, the other showing how skillful the forecast is. It was also found that for the six-hourly forecasts the error distributions remain similar with forecast lead time but skill decreases. This highlights the difference between forecast error and forecast skill, and that they are not necessarily the same.

Spatial verification using wavelet transforms: a review

“…Mittermaier () studies the impact of observational uncertainties on forecast skill and error using traditional verification measures such as frequency bias, equitable threat score, ROC curve and log odds ratio, as well as the ISS. 6 and 24 h precipitation accumulations of the mesoscale Met Office UM are evaluated against radar data.…”

An investigation of the error budget of tropical rainfall accumulation derived from merged passive microwave and infrared satellite measurements

“…On the other hand, at meteorologically relevant scales (<10 days, < 1° ), corresponding to a certain extent to some large basin hydrological scales, emerging applications like hydrology/crop model alimentation (Teo, 2006), meteorological quantitative precipitation forecasts evaluation (e.g. Bowler, 2006; Candille and Talagrand, 2008; Mittermaier, 2008; Ghelli and Santos, 2010), fine‐scale analysis of water cycle processes (Roca et al , 2010b), or simply validation efforts based on ground measurements (e.g. R2010), all require an estimation of rainfall with an error associated with the accumulation.…”

A critical assessment of surface cloud observations and their use for verifying cloud forecasts

“…The uncertainties in the observations are often disregarded. Bowler (2006) and Mittermaier (2008) began to explore how observation uncertainty can be incorporated into verification metrics, and the impact these uncertainties may have. It is important to recognise that observations of the same atmospheric quantity may vary across the observing network, in terms of type (automated or manual), as well as instrument type (i.e.…”