ALBERT

Description: Probabilistic quantitative precipitation forecasts (PQPFs) were generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database from July to October 2010 using data from Europe (ECMWF), the United Kingdom [Met Office (UKMO)], the United States (NCEP), and Canada [Canadian Meteorological Centre (CMC)]. Forecasts of 24-h accumulated precipitation were evaluated at 1° grid spacing within the contiguous United States against analysis data based on gauges and bias-corrected radar data. PQPFs from ECMWF’s ensembles generally had the highest skill of the raw ensemble forecasts, followed by CMC. Those of UKMO and NCEP were less skillful. PQPFs from CMC forecasts were the most reliable but the least sharp, and PQPFs from NCEP and UKMO ensembles were the least reliable but sharper. Multimodel PQPFs were more reliable and skillful than individual ensemble prediction system forecasts. The improvement was larger for heavier precipitation events [e.g., 〉10 mm (24 h)−1] than for smaller events [e.g., 〉1 mm (24 h)−1]. ECMWF ensembles were statistically postprocessed using extended logistic regression and the five-member weekly reforecasts for the June–November period of 2002–09, the period where precipitation analyses were also available. Multimodel ensembles were also postprocessed using logistic regression and the last 30 days of prior forecasts and analyses. The reforecast-calibrated ECMWF PQPFs were much more skillful and reliable for the heavier precipitation events than ECMWF raw forecasts but much less sharp. Raw multimodel PQPFs were generally more skillful than reforecast-calibrated ECMWF PQPFs for the light precipitation events but had about the same skill for the higher-precipitation events; also, they were sharper but somewhat less reliable than ECMWF reforecast-based PQPFs. Postprocessed multimodel PQPFs did not provide as much improvement to the raw multimodel PQPF as the reforecast-based processing did to the ECMWF forecast. The evidence presented here suggests that all operational centers, even ECMWF, would benefit from the open, real-time sharing of precipitation forecast data and the use of reforecasts.

Description: This manuscript presents a detailed multiscale analysis—using observations, model analyses, and ensemble forecasts—of the extreme heat wave over Russia and historic floods over Pakistan during late July 2010, with an emphasis on the floods over northern Pakistan. The results show that recirculation of air and dynamically driven subsidence occurring with the intensification of the blocking anticyclone in late July 2010 were key factors for producing the exceptionally warm temperatures over western Russia. Downstream energy dispersion from the blocking region led to trough deepening northwest of Pakistan and ridge building over the Tibetan Plateau, thereby providing the linkage between the Russian heat wave and Pakistan flood events on the large scale, in agreement with previous studies. The extratropical downstream energy dispersion and enhanced convective outflow on the large scale associated with the active phase of the Madden–Julian oscillation facilitated the formation of an intense upper-level jet northwest of Pakistan. During this same period an intense southeasterly, low-level, barrier jet–like feature formed over northern Pakistan in conjunction with a westward-moving monsoon depression. This low-level jet and deep easterly flow on the equatorward flank of an anomalous anticyclone over the Tibetan Plateau further enhanced the transport of deep tropical moisture into Pakistan and produced a sustained upslope flow and an extended period of active convection, thereby providing an important contribution leading to the exceptional rainfall amounts. The deep easterly flow and intense low-level jet were features that were absent during previous convective episodes over northern Pakistan in 2010, and hence, were likely key factors in the increased severity of the late July event.

Description: Inflation of ensemble perturbations is employed in ensemble Kalman filters to account for unrepresented error sources. The authors propose a multiplicative inflation algorithm that inflates the posterior ensemble in proportion to the amount that observations reduce the ensemble spread, resulting in more inflation in regions of dense observations. This is justified since the posterior ensemble variance is more affected by sampling errors in these regions. The algorithm is similar to the “relaxation to prior” algorithm proposed by Zhang et al., but it relaxes the posterior ensemble spread back to the prior instead of the posterior ensemble perturbations. The new inflation algorithm is compared to the method of Zhang et al. and simple constant covariance inflation using a two-level primitive equation model in an environment that includes model error. The new method performs somewhat better, although the method of Zhang et al. produces more balanced analyses whose ensemble spread grows faster. Combining the new multiplicative inflation algorithm with additive inflation is found to be superior to either of the methods used separately. Tests with large and small ensembles, with and without model error, suggest that multiplicative inflation is better suited to account for unrepresented observation-network-dependent assimilation errors such as sampling error, while model errors, which do not depend on the observing network, are better treated by additive inflation. A combination of additive and multiplicative inflation can provide a baseline for evaluating more sophisticated stochastic treatments of unrepresented background errors. This is demonstrated by comparing the performance of a stochastic kinetic energy backscatter scheme with additive inflation as a parameterization of model error.