Spurious Grid-Scale Precipitation in the North American Regional Reanalysis

Abstract

Spurious grid-scale precipitation (SGSP) occurs in many mesoscale numerical weather prediction models when the simulated atmosphere becomes convectively unstable and the convective parameterization fails to relieve the instability. Case studies presented in this paper illustrate that SGSP events are also found in the North American Regional Reanalysis (NARR) and are accompanied by excessive maxima in grid-scale precipitation, vertical velocity, moisture variables (e.g., relative humidity and precipitable water), mid- and upper-level equivalent potential temperature, and mid- and upper-level absolute vorticity. SGSP events in environments favorable for high-based convection can also feature low-level cold pools and sea level pressure maxima. Prior to 2003, retrospectively generated NARR analyses feature an average of approximately 370 SGSP events annually. Beginning in 2003, however, NARR analyses are generated in near–real time by the Regional Climate Data Assimilation System (R-CDAS), which is identical to the retrospective NARR analysis system except for the input precipitation and ice cover datasets. Analyses produced by the R-CDAS feature a substantially larger number of SGSP events with more than 4000 occurring in the original 2003 analyses. An oceanic precipitation data processing error, which resulted in a reprocessing of NARR analyses from 2003 to 2005, only partially explains this increase since the reprocessed analyses still produce approximately 2000 SGSP events annually. These results suggest that many NARR SGSP events are not produced by shortcomings in the underlying Eta Model, but by the specification of anomalous latent heating when there is a strong mismatch between modeled and assimilated precipitation. NARR users should ensure that they are using the reprocessed NARR analyses from 2003 to 2005 and consider the possible influence of SGSP on their findings, particularly after the transition to the R-CDAS.

Hydroclimate and variability in the Great Lakes region as derived from the North American Regional Reanalysis

“…[36] Finally, it is worth pointing out that although the NARR data are perhaps the best gridded data available for the tasks undertaken in the current study, they are only estimates of the true atmospheric hydrological state and they contain errors and uncertainties, as documented by recent validation studies [Mo et al, 2005;Mesinger et al, 2006;RuizBarradas and Nigam, 2006;West et al, 2007]. One of the uncertainties regarding surface precipitation is what appears to be an artificial decrease of NARR summer season precipitation at the U.S.-Canadian border due to differences in the precipitation assimilation data sets between the United States and Canada (Figure 10).…”

Evaluation of extreme rainfall and temperature over North America in CanRCM4 and CRCM5

“…However several issues with NARR have been identified. Spurious grid-scale precipitation, an issue for many mesoscale climate and weather models, has also been identified in NARR (West et al 2007). There is an over-estimation of the Gulf of California low-level jet in summer (Mo et al 2005) and an underestimation of precipitation in northern oceanic cyclonic regions (Mesinger et al 2006).…”

“…Typically, precipitation would be considered to be a 'Type C' variable. However, in NARR, precipitation observations are assimilated to adjust accumulated convective and grid-scale precipitation, latent heating, moisture and cloud fields based on differences between modelled and observed hourly precipitation estimates (West et al 2007). This ensures a more realistic representation of precipitation than if precipitation forecasts were based solely on the model (Mesinger et al 2006), and renders a representation of precitation that is arguably better than 'Type C' over at least parts of the NARR domain.…”

High-Resolution Downscaled Simulations of Warm-Season Extreme Precipitation Events in the Colorado Front Range under Past and Future Climates*

“…The spatial structure of a given RCM's past and future precipitation are more similar to one another than to that of other models or that of NARR, indicating that each model's preferred regional precipitation pattern is reasonably sampled by 10 events. NARR may also have difficulty representing large precipitation amounts in mountainous terrain and has other known limitations and errors that have been documented as well (e.g., Mesinger et al 2006;Bukovsky and Karoly 2007;West et al 2007;Dominguez et al 2012;Hughes et al 2012). Thus, the comparison is offered here as a basic benchmark against a proxy for observations, but deviations between NARR and the RCM top 10 events not only reflect model bias in pattern and magnitude but also the independence of the events considered.…”