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Madalina Surcel, Isztar Zawadzki, and M. K. Yau

Abstract

A methodology is proposed to investigate the scale dependence of the predictability of precipitation patterns at the mesoscale. By applying it to two or more precipitation fields, either modeled or observed, a decorrelation scale can be defined such that all scales smaller than are fully decorrelated. For precipitation forecasts from a radar data–assimilating storm-scale ensemble forecasting (SSEF) system, is found to increase with lead time, reaching 300 km after 30 h. That is, for , the ensemble members are fully decorrelated. Hence, there is no predictability of the model state for these scales. For , the ensemble members are correlated, indicating some predictability by the ensemble. When applied to characterize the ability to predict precipitation as compared to radar observations by numerical weather prediction (NWP) as well as by Lagrangian persistence and Eulerian persistence, increases with lead time for most forecasting methods, while it is constant (300 km) for non–radar data–assimilating NWP.

Comparing the different forecasting models, it is found that they are similar in the 0–6-h range and that none of them exhibit any predictive ability at meso-γ and meso-β scales after the first 2 h. On the other hand, the radar data–assimilating ensemble exhibits predictability of the model state at these scales, thus causing a systematic difference between corresponding to the ensemble and corresponding to model and radar. This suggests that either the ensemble does not have sufficient spread at these scales or that the forecasts suffer from biases.

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Madalina Surcel, Marc Berenguer, and Isztar Zawadzki

Abstract

The diurnal cycle of precipitation over the continental United States is characterized through the analysis of radar rainfall maps and is used as a measure of performance of the Global Environmental Multiscale (GEM) model during the spring (April–May) and summer (July–August) of 2008. The main interest is to determine the effects of different types of forcing (synoptic versus thermal) on the average daily variability of precipitation and on the model’s representation of it. A secondary objective is to study the interannual variability of the diurnal cycle. The investigation is based on the analysis of time–longitude diagrams of precipitation fields, of average statistics, and of model skill scores.

The results show that the main differences between the spring and summer diurnal cycles are the duration of propagating systems, the frequency of convective events in the southeastern United States, and more interannual variability of the spring diurnal cycle. However, most interesting is that the timing of precipitation initiation over the Rockies is in phase with the cycle of solar warming for both seasons, despite the strong synoptic forcing during spring. Also, east of the Rockies, the diurnal cycle is mainly determined by transport mechanism and is consequently out of phase with the solar cycle.

While GEM represents fairly well the timing of precipitation initiation along the Rockies during both seasons, it fails to correctly depict the propagation characteristics of these systems. During spring, the simulated systems show more variability in propagation paths than observed, while during summer, the observed propagation is simply not captured by GEM. This is probably a consequence of different propagation mechanisms acting in the model and in the atmosphere, and between spring and summer.

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Madalina Surcel, Isztar Zawadzki, and M. K. Yau

Abstract

This paper analyzes the case-to-case variability of the predictability of precipitation by a storm-scale ensemble forecasting (SSEF) system. Relationships are sought between ensemble spread and quantitative precipitation forecast (QPF) skill, and the characteristics of an event, such as the strength of the quasigeostrophic forcing for ascent, the presence of convective equilibrium, and the spatial extent of the precipitation system. It is found that most of the case-to-case variability of predictability is explained by the spatial coverage of the system. The relationship between convection and large-scale forcing seems to affect predictability mostly during the afternoon hours. While the relationships are weak for the entire dataset, two distinct types of cases are identified: widespread and diurnally forced cases. The loss of predictability at small scales, the effect of the radar data assimilation, and the comparison between forecasts from the SSEF and Lagrangian persistence forecasts are analyzed separately for these two types of cases. Despite overall predictability being better than average for the widespread cases, the loss of predictability with forecast time and spatial scale is just as rapid as for the other cases. For the diurnally forced cases, the radar data assimilation causes larger differences between the precipitation fields corresponding to the assimilating and nonassimilating members than for the widespread cases. However, the effect of radar data assimilation on QPF skill is similar for both types of cases. Also, for the diurnal cases, the models with radar data assimilation outperform very rapidly (after 2 h) the Lagrangian persistence forecasts.

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Madalina Surcel, Isztar Zawadzki, and M. K. Yau

Abstract

The mean (ENM) of an ensemble of precipitation forecasts is generally more skillful than any of the members as verified against observations. A major reason is that the averaging filters out nonpredictable features on which the members disagree. Previous research showed that the nonpredictable features occur at small scales, in both numerical forecasts and Lagrangian persistence nowcasts. Hence, it is plausible that the unpredictable features filtered through ensemble averaging would also occur at small scales. In this study, the exact range of scales affected by averaging is determined by comparing the statistical properties of precipitation fields between the ENM and the individual members from a Storm-Scale Ensemble Forecasting (SSEF) system run during NOAA’s 2008 Hazardous Weather Testbed (HWT) Spring Experiment. The filtering effect of ensemble averaging results in a low-intensity bias for the ENM forecasts. It has been previously proposed to correct the ENM forecasts by recalibrating the intensities in the ENM using the probability density function (PDF) of rainfall values from the ensemble members. This procedure, probability matching (PM), leads to a new ensemble mean, the probability matched mean (PMM). Past studies have shown that the PMM appears more realistic and yields better skill as evaluated using traditional scores. However, the authors demonstrate here that despite the PMM having the same PDF of rainfall intensities as the ensemble members, the spectral structure and the spatial distribution of the precipitation field differs from that of the members. It is the lesser variability of the PMM fields at small scales that causes the better scores of the PMM relative to the ensemble members.

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Marc Berenguer, Madalina Surcel, Isztar Zawadzki, Ming Xue, and Fanyou Kong

Abstract

This second part of a two-paper series compares deterministic precipitation forecasts from the Storm-Scale Ensemble Forecast System (4-km grid) run during the 2008 NOAA Hazardous Weather Testbed (HWT) Spring Experiment, and from the Canadian Global Environmental Multiscale (GEM) model (15 km), in terms of their ability to reproduce the average diurnal cycle of precipitation during spring 2008. Moreover, radar-based nowcasts generated with the McGill Algorithm for Precipitation Nowcasting Using Semi-Lagrangian Extrapolation (MAPLE) are analyzed to quantify the portion of the diurnal cycle explained by the motion of precipitation systems, and to evaluate the potential of the NWP models for very short-term forecasting.

The observed diurnal cycle of precipitation during spring 2008 is characterized by the dominance of the 24-h harmonic, which shifts with longitude, consistent with precipitation traveling across the continent. Time–longitude diagrams show that the analyzed NWP models partially reproduce this signal, but show more variability in the timing of initiation in the zonal motion of the precipitation systems than observed from radar.

Traditional skill scores show that the radar data assimilation is the main reason for differences in model performance, while the analyzed models that do not assimilate radar observations have very similar skill.

The analysis of MAPLE forecasts confirms that the motion of precipitation systems is responsible for the dominance of the 24-h harmonic in the longitudinal range 103°–85°W, where 8-h MAPLE forecasts initialized at 0100, 0900, and 1700 UTC successfully reproduce the eastward motion of rainfall systems. Also, on average, MAPLE outperforms radar data assimilating models for the 3–4 h after initialization, and nonradar data assimilating models for up to 5 h after initialization.

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Madalina Surcel, Isztar Zawadzki, M. K. Yau, Ming Xue, and Fanyou Kong

Abstract

This paper analyzes the scale and case dependence of the predictability of precipitation in the Storm-Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms (CAPS) during the NOAA Hazardous Weather Testbed Spring Experiments of 2008–13. The effect of different types of ensemble perturbation methodologies is quantified as a function of spatial scale. It is found that uncertainties in the large-scale initial and boundary conditions and in the model microphysical parameterization scheme can result in the loss of predictability at scales smaller than 200 km after 24 h. Also, these uncertainties account for most of the forecast error. Other types of ensemble perturbation methodologies were not found to be as important for the quantitative precipitation forecasts (QPFs). The case dependences of predictability and of the sensitivity to the ensemble perturbation methodology were also analyzed. Events were characterized in terms of the extent of the precipitation coverage and of the convective-adjustment time scale , an indicator of whether convection is in equilibrium with the large-scale forcing. It was found that events characterized by widespread precipitation and small values (representative of quasi-equilibrium convection) were usually more predictable than nonequilibrium cases. No significant statistical relationship was found between the relative role of different perturbation methodologies and precipitation coverage or .

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Phillipa Cookson-Hills, Daniel J. Kirshbaum, Madalina Surcel, Jonathan G. Doyle, Luc Fillion, Dominik Jacques, and Seung-Jong Baek

Abstract

Environment and Climate Change Canada (ECCC) has recently developed an experimental high-resolution EnKF (HREnKF) regional ensemble prediction system, which it tested over the Pacific Northwest of North America for the first half of February 2011. The HREnKF has 2.5-km horizontal grid spacing and assimilates surface and upper-air observations every hour. To determine the benefits of the HREnKF over less expensive alternatives, its 24-h quantitative precipitation forecasts are compared with those from a lower-resolution (15 km) regional ensemble Kalman filter (REnKF) system and to ensembles directly downscaled from the REnKF using the same grid as the HREnKF but with no additional data assimilation (DS). The forecasts are verified against rain gauge observations and gridded precipitation analyses, the latter of which are characterized by uncertainties of comparable magnitude to the model forecast errors. Nonetheless, both deterministic and probabilistic verification indicates robust improvements in forecast skill owing to the finer grids of the HREnKF and DS. The HREnKF exhibits a further improvement in performance over the DS in the first few forecast hours, suggesting a modest positive impact of data assimilation. However, this improvement is not statistically significant and may be attributable to other factors.

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