[Eoas-seminar] Meteorology Doctoral Defense - Mark Nissenbaum - June 25, 3:00pm - Zoom

[eoas-seminar at lists.fsu.edu]

Meteorology Seminar

Mark Nissenbaum

Meteorology Doctoral Candidate



Title:  Identification, Climatology, and Predictability of Observed and Modeled Mesoscale Snowbands

Major Professor:  Dr. Robert Hart

Date: Friday, June 25, 2021                        Time: 3:00pm


Location: Zoom   https://fsu.zoom.us/j/97148654298



ABSTRACT

      Mesoscale precipitation bands were investigated in 38 winter storms that impacted the northeast US between 2004 and 2015 in the GridRad reflectivity mosaic dataset and compared to simulated bands in the 12 km North American Mesoscale Model (NAM) at all forecast times. A total of 921 bands were identified in GridRad and 3681 bands were identified in NAM. Bands were classified by their appearance into embedded, isolated, broken, merged, fine scale, or heavy precipitation band types. These band types were further classified into multi-bands if two or more bands occurred alongside each other with similar band orientation angles and appearances.

      Embedded bands were the most common band type, representing 72.4% of all bands in the radar dataset and 87.7% of all bands in the NAM for all storm cases. The remaining band types were well-represented in the radar mosaic but were captured less often in the NAM, likely due to the coarse model grid. The composite thermodynamic environment of the observed bands was studied using the North American Regional Reanalysis (NARR), but the band types contained overlapping confidence intervals and lacked quantitative independence. However, slantwise convective available potential energy (SCAPE) was significantly correlated with the orientation angle of the bands, with large values of SCAPE in northeasterly tilted bands compared to northwesterly tilted bands. As some bands pivot cyclonically with time, there is a natural tendency for northwesterly tilted bands to appear later in the cyclone lifecycle, suggesting that band angle can serve as a proxy for the stage of cyclone development. Thus, SCAPE is maximized in northeasterly tilted bands early in the cyclone lifecycle when baroclinicity is as its strongest, and less SCAPE is available for northwesterly tilted bands later in the cyclone lifecycle when horizontal thermal gradients weaken.

      The performance of NAM was evaluated against the observed bands in GridRad. NAM poorly resolved the observed bands at the analysis time and had even lower performance at greater lead times. By forecast hour 72, the threat score (TS) values fluctuated around 0, suggesting that almost all model skill regarding the prediction of mesoscale bands is lost. Model performance also varied according to the stage of cyclone development. TS values improved in the 5-20 hours following occlusion compared to the hours before and during occlusion. To better understand the factors that affect model performance, the environment composing low and high predictability events was investigated. The low predictability environment consists of a single upper-level jet south of the surface low, while the high predictability environment consists of a surface low favorably positioned within the ascending branch of two robust upper-level jet streaks. A case study of a low predictability event demonstrated rapid error growth in the strength, position, and amplitude of the upper-level trough at forecast hours as early as 30 hours prior to the banding event, with large biases in the strength and position of the trough and ridge at the analysis time. In comparison, a case study of a high predictability event achieved similar levels of error growth as late as 60 hours prior to the banding event, with smaller error growth at earlier lead times. This would suggest that an important predictability window exists at around 30 hours. In general, low predictability events are characterized by low centers that are displaced southwest of the verified low center locations. The successful prediction of mesoscale bands in numerical weather prediction models remains a challenging task.

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