Mesoscale Predictability of the “Surprise” Snowstorm of 24–25 January 2000

Zhang, F.; Snyder, Chris; Rotunno, Richard
June 2002
Monthly Weather Review;Jun2002, Vol. 130 Issue 6, p1617
Academic Journal
A mesoscale model is used here to investigate the possible sources of forecast error for the 24–25 January 2000 snowstorm along the east coast of the United States. The primary focus is the quantitative precipitation forecast out to lead times of 36 h. The success of the present high-resolution control forecast shows that the storm could have been well forecasted with conventional data in real time. Various experiments suggest that insufficient model grid resolution and errors in the initial conditions both contributed significantly to problems in the forecast. Other experiments, motivated by the possibility that the forecast errors arose from the operational analysis poorly fitting one or two key soundings, test the effects of withholding single soundings from the control initial conditions. While no single sounding results in forecast changes that are more than a small fraction of the error in the operational forecast, these experiments do reveal that the detailed mesoscale distribution of precipitation in the 24- or 36-h forecast can be significantly altered even by such small changes in the initial conditions. The experiments also reveal that the forecast changes arise from the rapid growth of error at scales below 500 km in association with moist processes. The results presented emphasize the difficulty of forecasting precipitation relative to, say, surface pressure and suggest that the predictability of mesoscale precipitation features in cases of the type studied here may be limited to less than 2–3 days.


Related Articles

  • Application of the Composite Method to the Spatial Forecast Verification Methods Intercomparison Dataset. Nachamkin, Jason E. // Weather & Forecasting;Oct2009, Vol. 24 Issue 5, p1390 

    The composite method is applied to verify a series of idealized and real precipitation forecasts as part of the Spatial Forecast Verification Methods Intercomparison Project. The test cases range from simple geometric shapes to high-resolution (∼4 km) numerical model precipitation output....

  • Relationship between Precipitation Forecast Errors and Skill Scores of Dichotomous Forecasts. Tartaglione, Nazario // Weather & Forecasting;Feb2010, Vol. 25 Issue 1, p355 

    In this paper, the sensitivities of the equitable threat score (ETS) and the true skill score (TSS), obtained with a 2 × 2 contingency table, to continuous precipitation forecast errors are investigated. Two idealized error models are adopted to describe the difference between forecasts and...

  • Contributions of Mixed Physics versus Perturbed Initial/Lateral Boundary Conditions to Ensemble-Based Precipitation Forecast Skill. Clark, Adam J.; Gallus Jr., William A.; Chen, Tsing-Chang // Monthly Weather Review;Jun2008, Vol. 136 Issue 6, p2140 

    An experiment is described that is designed to examine the contributions of model, initial condition (IC), and lateral boundary condition (LBC) errors to the spread and skill of precipitation forecasts from two regional eight-member 15-km grid-spacing Weather Research and Forecasting (WRF)...

  • Sampling Error Damping Method for a Cloud-Resolving Model Using a Dual-Scale Neighboring Ensemble Approach. Aonashi, Kazumasa; Okamoto, Kozo; Tashima, Tomoko; Kubota, Takuji; Ito, Kosuke // Monthly Weather Review;Dec2016, Vol. 144 Issue 12, p4751 

    In ensemble-based assimilation schemes for cloud-resolving models (CRMs), the precipitation-related variables have serious sampling errors. The purpose of the present study is to examine the sampling error properties and the forecast error characteristics of the operational CRM of the Japan...

  • Bias adjustment for decadal predictions of precipitation in Europe from CCLM. Li, Jingmin; Pollinger, Felix; Panitz, Hans-Juergen; Feldmann, Hendrik; Paeth, Heiko // Climate Dynamics;Aug2019, Vol. 53 Issue 3/4, p1323 

    A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment...

  • Application of Spatial Verification Methods to Idealized and NWP-Gridded Precipitation Forecasts. Ahijevych, David; Gilleland, Eric; Brown, Barbara G.; Ebert, Elizabeth E. // Weather & Forecasting;Dec2009, Vol. 24 Issue 6, p1485 

    Several spatial forecast verification methods have been developed that are suited for high-resolution precipitation forecasts. They can account for the spatial coherence of precipitation and give credit to a forecast that does not necessarily match the observation at any particular grid point....

  • Improvements in quantitative precipitation forecasts with the eta regional model at the National... Mesinger, Fedor // Bulletin of the American Meteorological Society;Nov96, Vol. 77 Issue 11, p2637 

    Compares improvements in quantitative precipitation forecasts using the National Centers for Environmental Prediction's (NCEP) operational models, the eta coordinate regional model and the Nested Grid Model (NGM). Advantages of the eta regional model over NGM; Successful forecast of Hurricane...

  • Impact of Verification Grid-Box Size on Warm-Season QPF Skill Measures. Gallus, William A. // Weather & Forecasting;Dec2002, Vol. 17 Issue 6, p1296 

    A 10-km-grid-spacing version of NCEP's Eta Model was used to simulate 11 warm-season convective systems occurring over the U.S. upper midwest. Quantitative precipitation forecasts (QPFs) from the model valid for 6h periods were verified using 4-km-grid-spacing stage-IV precipitation estimates....

  • Daily Precipitation Forecasting in Dakar Using the NCEP–NCAR Reanalyses. Deme, Abdoulaye; Viltard, Alain; de Félice, Pierre // Weather & Forecasting;Feb2003, Vol. 18 Issue 1, p93 

    In order to predict the daily rain amount at Dakar at 1-5-day lead times, 65 thermodynamical and dynamical indices are computed at each grid point for the area 15°S-30°N, 30°W-30°E. The data used are NCEP-NCAR reanalyses and daily rainfall obtained by averaging over 21 rain gauges...


Read the Article

Courtesy of

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics