366551 Assessing the Robustness of Microphysical Process Representation in an Adaptive Habit Model by Means of Stochastic Parameterizations

Tuesday, 14 January 2020
Hall B1 (Boston Convention and Exhibition Center)
Lauriana C. Gaudet, Univ. at Albany, SUNY, Albany, NY; and K. Sulia

A two-dimensional stochastic perturbed parameterization (SPP) is implemented into the Weather Research and Forecasting (WRF) Model to perturb vapor deposition within an ice crystal habit evolving microphysics model. Several tuning experiments with slightly different (about 30%) spatial, temporal, and amplitude autocorrelation parameters are conducted to elicit those most conducive to the production of physically-sound ensemble spread (i.e., standard deviation). These parameters are used, along with random number seeds, to generate a stochastic pattern that perturbs the process rate, thereby producing a microphysical ensemble. The ensemble spread is assessed using atmospheric state variables, such as 2-m temperature, and various statistical tests, including rank histograms. The performance of the ensemble is verified against the New York State mesonet observations of temperature, wind, and precipitation. This methodology is used to investigate the impact of vapor deposition on high-intensity precipitation with a tropical moisture source that impacted eastern New York State from 29-30 October 2017. Preliminary results indicate that reasonable forecast spread is produced with the perturbation of vapor deposition. However, that specific process rate is not the one that contributed the greatest amount of spread. Interestingly, this spread is attributed to one of several process rates that are not explicitly perturbed within the model, suggesting that there are cascading processes occurring within the system. Identification of processes, if any, that considerably increase forecast spread, is extremely important for high-intensity rainfall forecasts due to the associated potential of societal impacts. As a whole, this research contributes new knowledge about precipitation responses to the representation of microphysics in numerical weather prediction models.
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