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Accurate quantitative precipitation estimates (QPEs) at high spatial and temporal resolution are difficult to obtain in regions of complex terrain due to the large spatial heterogeneity of orographically enhanced precipitation, sparsity of gauges, precipitation phase variations, and terrain effects that impact the quality of remotely sensed estimates. The large uncertainty of QPE in these regions also makes the evaluation of high-resolution quantitative precipitation forecasts (QPFs) challenging, as it can be difficult to choose a reference QPE that is reliable at both high and low elevations. In this paper we demonstrate a methodology to combine information from multiple high-resolution hourly QPE products to evaluate QPFs from NOAA's High-Resolution Rapid Refresh (HRRR) model in a region of Northern California. The methodology uses the quantiles of monthly QPE distributions to determine a range of hourly precipitation that correspond to "good," "possible," "underestimated," or "overestimated" QPFs. In this manuscript, we illustrate the use of the methodology to evaluate QPFs for seven atmospheric river events that occurred during the 2016–17 wet season in Northern California. Because the presence of frozen precipitation is often not captured by traditional QPE products, we evaluate QPFs both for all precipitation, and with likely frozen precipitation excluded. The methodology is shown to provide useful information to evaluate model performance while taking into account the uncertainty of available QPE at various temporal and spatial scales. The potential of the technique to evaluate changes between model versions is also shown. [ABSTRACT FROM AUTHOR]

NOAA's second-generation reforecasts are approximately consistent with the operational version of the 2012 NOAA Global Ensemble Forecast System (GEFS). The reforecasts allow verification to be performed across a multidecadal time period using a static model, in contrast to verifications performed using an ever-evolving operational modeling system. This contribution examines three commonly used verification metrics for reforecasts of precipitation over the southeastern United States: equitable threat score, bias, and ranked probability skill score. Analysis of the verification metrics highlights the variation in the ability of the GEFS to predict precipitation across amount, season, forecast lead time, and location. Beyond day 5.5, there is little useful skill in quantitative precipitation forecasts (QPFs) or probabilistic QPFs. For lighter precipitation thresholds [e.g., 5 and 10 mm (24 h)−1], use of the ensemble mean adds about 10% to the forecast skill over the deterministic control. QPFs have increased in accuracy from 1985 to 2013, likely due to improvements in observations. Results of this investigation are a first step toward using the reforecast database to distinguish weather regimes that the GEFS typically predicts well from those regimes that the GEFS typically predicts poorly. [ABSTRACT FROM AUTHOR]

The use of the potential vorticity (PV) framework by operational forecasters is advocated through case examples that demonstrate its utility for interpreting and evaluating numerical weather prediction (NWP) model output for weather systems characterized by strong latent heat release (LHR). The interpretation of the dynamical influence of LHR is straightforward in the PV framework; LHR can lead to the generation of lower-tropospheric cyclonic PV anomalies. These anomalies can be related to meteorological phenomena including extratropical cyclones and low-level jets (LLJs), which can impact lower-tropospheric moisture transport. The nonconservation of PV in the presence of LHR results in a modification of the PV distribution that can be identified in NWP model output and evaluated through a comparison with observations and high-frequency gridded analyses. This methodology, along with the application of PV-based interpretation, can help forecasters identify aspects of NWP model solutions that are driven by LHR; such features are often characterized by increased uncertainty due to difficulties in model representation of precipitation amount and latent heating distributions, particularly for convective systems. Misrepresentation of the intensity and/or distribution of LHR in NWP model forecasts can generate errors that propagate through the model solution with time, potentially degrading the representation of cyclones and LLJs in the model forecast. The PV framework provides human forecasters with a means to evaluate NWP model forecasts in a way that facilitates recognition of when and how value may be added by modifying NWP guidance. This utility is demonstrated in case examples of coastal extratropical cyclogenesis and LLJ enhancement. Information is provided regarding tools developed for applying PV-based techniques in an operational setting. [ABSTRACT FROM AUTHOR]

Operational forecasters in the southeast and mid-Atlantic regions of the United States have noted a positive quantitative precipitation forecast (QPF) bias in numerical weather prediction (NWP) model forecasts downstream of some organized, cold-season convective systems. Examination of cold-season cases in which model QPF guidance exhibited large errors allowed identification of two representative cases for detailed analysis. The goals of the case study analyses are to (i) identify physical mechanisms through which the upstream convection (UC) alters downstream precipitation amounts, (ii) determine why operational models are challenged to provide accurate guidance in these situations, and (iii) suggest future research directions that would improve model forecasts in these situations and allow forecasters to better anticipate such events. Two primary scenarios are identified during which downstream precipitation is altered in the presence of UC for the study region: (i) a fast-moving convective (FC) scenario in which organized convective systems oriented parallel to the lower-tropospheric flow are progressive relative to the parent synoptic system, and appear to disrupt poleward moisture transport, and (ii) a situation characterized by slower-moving convection (SC) relative to the parent system. Analysis of a representative FC case indicated that moisture consumption, stabilization via convective overturning, and modification of the low-level flow to a more westerly direction in the postconvective environment all appear to contribute to the reduction of downstream precipitation. In the FC case, operational Eta Model forecasts moved the organized UC too slowly, resulting in an overestimate of downstream moisture transport. A 4-km explicit convection model forecast from the Weather Research and Forecasting model produced a faster-moving upstream convective system and improved downstream QPF. In contrast to the FC event, latent heat release in the primary rainband is shown to enhance the low-level jet ahead of the convection in the SC case, thereby increasing moisture transport into the downstream region. A negative model QPF bias was observed in Eta Model forecasts for the SC event. Suggestions are made for precipitation forecasting in UC situations, and implications for NWP model configuration are discussed. [ABSTRACT FROM AUTHOR]

The sensitivity of numerical model forecasts of coastal cyclogenesis and frontogenesis to the choice of model cumulus parameterization (CP) scheme is examined for the 17 February 2004 southeastern U.S. winter weather event. This event featured a complex synoptic and mesoscale environment, as the presence of cold-air damming, a developing coastal surface cyclone, and an upper-level trough combined to present a daunting winter weather forecast scenario. The operational forecast challenge was further complicated by erratic numerical model predictions. The most poignant area of disagreement between model runs was the treatment of a coastal cyclone and an associated coastal front, features that would affect the location and timing of precipitation and influence the precipitation type. At the time of the event, it was hypothesized that the Betts–Miller–Janjić (BMJ) CP scheme was dictating the location and intensity of the initial coastal cyclone center in operational Eta Model forecasts. For this reason, forecasts for this case were rerun with the workstation Eta Model using the Kain–Fritsch (KF) CP scheme to further examine the sensitivity to this parameterization choice. Results confirm that the model CP scheme played a major role in the forecast for this case, affecting the quantitative precipitation forecast as well as the strength, location, and structure of coastal cyclogenesis and coastal frontogenesis. The Eta Model forecast using the KF CP scheme produced a relatively uniform distribution of convective precipitation oriented along the axis of an inverted trough and strong coastal front. In contrast, the BMJ forecasts resulted in a weaker coastal front and the development of multiple distinct closed cyclonic circulations in association with more localized convective precipitation centers. An additional BMJ forecast in which the shallow mixing component of the scheme was disabled bore a closer semblance to the KF forecasts relative to the original BMJ forecast. Suggestions are provided to facilitate the identification of CP-driven cyclones using standard operational model output parameters. [ABSTRACT FROM AUTHOR]