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Hae-Lim Kim, Sung-Hwa Jung, and Kun-Il Jang

Abstract

Raindrop size distribution (DSD) observed using a disdrometer can be represented by a constrained-gamma (C-G) DSD model based on the empirical relationship between shape (µ) and slope (Λ). The C-G DSD model can be used to retrieve DSDs and rain microphysical parameters from dual-polarization radar measurements of reflectivity (Z H) and differential reflectivity (Z DR). This study presents a new µ–Λ relationship to characterize rain microphysics in South Korea using a two-dimensional video disdrometer (2DVD) and Yong-in S-band dual-polarization radar. To minimize sampling errors from the 2DVD and radar measurements, measured size distributions are truncated by particle size and velocity-based filtering and compared with rain gauge measurement. The calibration biases of radar Z H and Z DR were calculated using the self-consistency constraint and vertical pointing measurements. The derived µ–Λ relationship was verified using the mass-weighted mean diameter (D m) and standard deviation of the size distribution (σ m), calculated from the 2DVD, for comparison with existing µ–Λ relationships for Florida and Oklahoma. The D mσ m relationship derived from the 2DVD corresponded well with the µ–Λ relationship. The µ–Λ relationship derived for the Korean Peninsula was similar to Florida, and both generally had larger µ values than Oklahoma for the same Λ. The derived µ–Λ relationship was applied to retrieve DSD parameters from polarimetric radar data, and the retrieved DSDs and derived physical parameters were evaluated and compared with the 2DVD measurements. The polarization radar-based C-G DSD model characterized rain microphysics more accurately than the exponential DSD model. The C-G DSD model based on the newly derived µ–Λ relationship performed the best at retrieving rain microphysical parameters.

Open access
Kun-Il Jang, X. Zou, M. S. F. V. De Pondeca, M. Shapiro, C. Davis, and A. Krueger

Abstract

In this study, a methodology is proposed for incorporating total column ozone data from the Total Ozone Mapping Spectrometer (TOMS) into the initial conditions of a mesoscale prediction model. Based on the strong correlation between vertical mean potential vorticity (MPV) and TOMS ozone (O3) that was found in middle latitudes at both 30- and 90-km resolutions, using either analyses or 24-h model forecasts, a statistical correlation model between O3 and MPV is employed for assimilating TOMS ozone in a four-dimensional variational data assimilation (4DVAR) procedure. A linear relationship between O3 and MPV is first assumed: O3 = α(MPV) + β. The constants α and β are then found by a regression method. The proposed approach of using this simple linear regression model for ozone assimilation is applied to the prediction of the 24–25 January 2000 East Coast winter storm. Three 4DVAR experiments are carried out assimilating TOMS ozone, radiosonde, or both types of observations. It is found that adjustments in model initial conditions assimilating only TOMS ozone data are confined to the upper levels and produce almost no impact on the prediction of the storm development. However, when TOMS ozone data are used together with radiosonde observations, a more rapid deepening of sea level pressure of the simulated storm is observed than with only radiosonde observations. The predicted track of the winter storm is also altered, moving closer to the coast. Using NCEP multisensor hourly rainfall data as verification, the 36-h forecasts with both TOMS ozone and radiosonde observations outperform the radiosonde-only experiments. These results indicate that TOMS ozone data contain valuable meteorological information, which can be used to improve cyclone prediction.

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