Impact of Data Assimilation on Forecasting Convection over the United Kingdom Using a High-Resolution Version of the Met Office Unified Model

Abstract

A high-resolution data assimilation system has been implemented and tested within a 4-km grid length version of the Met Office Unified Model (UM). A variational analysis scheme is used to correct larger scales using conventional observation types. The system uses two nudging procedures to assimilate high-resolution information: radar-derived surface precipitation rates are assimilated via latent heat nudging (LHN), while cloud nudging (CN) is used to assimilate moisture fields derived from satellite, radar, and surface observations. The data assimilation scheme was tested on five convection-dominated case studies from the Convective Storm Initiation Project (CSIP). Model skill was assessed statistically using radar-derived surface-precipitation hourly accumulations via a scale-dependent verification scheme. Data assimilation is shown to have a dramatic impact on skill during both the assimilation and subsequent forecast periods on nowcasting time scales. The resulting forecasts are also shown to be much more skillful than those produced using either a 12-km grid length version of the UM with data assimilation in place, or a 4-km grid-length UM version run using a 12-km state as initial conditions. The individual contribution to overall skill attributable to each data-assimilation component is investigated. Up until T 1 3 h, LHN has the greatest impact on skill. For later times, VAR, LHN, and CN contribute equally to the skill in predicting the spatial distribution of the heaviest rainfall; while VAR alone accounts for the skill in depicting distributions corresponding to lower accumulation thresholds. The 4-km forecasts tend to overpredict both the intensity and areal coverage of storms. While it is likely that the intensity bias is partially attributable to model error, both VAR and LHN clearly contribute to the overestimation of the areal extent. Future developments that may mitigate this problem are suggested.

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

“…The most common method in operational use at the time of writing is latent heat nudging (LHN: Jones and Macpherson, 1997;Leuenberger, 2005;Leuenberger and Rossa, 2007;Montmerle et al, 2007;Stephan et al, 2008;Dixon et al, 2009), although a variety of more advanced techniques have been studied in research contexts. Operational experience suggests that the impact of the assimilated radar data is often short-lived, e.g.…”

Nowcasting of precipitation by an NWP model using assimilation of extrapolated radar reflectivity

“…Therefore, the nudging method is still used to assimilate radar reflectivity data among other observations (e.g. Sokol andRezacova, 2006, 2009;Stephan et al, 2008;Dixon et al, 2009). The application of the nudging was also supported by our focus on the very short-range precipitation forecast because the time consumption of the 4D-Var and PF methods restricts their operational application for very short lead times.…”

Propagation of uncertainty from observing systems into NWP: COST‐731 Working Group 1

“…The most common method in operational use at the time of writing is latent heat nudging (LHN: Jones and Macpherson, 1997;Leuenberger, 2005;Leuenberger and Rossa, 2007;Stephan et al, 2008;Dixon et al, 2009), although a variety of more advanced techniques have been studied in research contexts (e.g. Jakubiak, 2008;Caumont et al, 2009).…”

“…For example, Stephan et al (2008) show a case study where assimilation of radar data continued to have a strong positive impact after 6 h of free forecast, but commented that this was 'one of the most successful cases with regard to the duration of forecast impact of LHN' within their 2-month trial period. Ducrocq et al (2002), Leuenberger (2005) and Dixon et al (2009), on the other hand, reported on several convective cases in which the data assimilation has a dramatic impact on skill during both the assimilation and subsequent forecast periods on nowcasting time scales. The fact that the impact of the radar data normally decreased rapidly in the first 4 h of the free forecast is likely to be linked to the short predictability of cumulus convection, an inappropriate representation of the convective environment, as well as to deficiencies in current data assimilation methods.…”