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Nucciarone, J. J.

Publications and source records attributed to Nucciarone, J. J..

Convective and stratiform rain: Multichannel microwave sensing over oceans

Measurements made by the Special Sensor Microwave/Imager (SSM/I) radiometer over the oceans, at 19, 37, and 85 GHz in dual polarization, are used to develop a model to classify rain into light-stratiform, moderately convective, and heavy convective types in the mesoscale convective systems (MCS). It is observed that the bulk of the 19- and 37-GHz data are linearly correlated with respect to one another, and generally increase together in brightness as the mean rain rate in the field of view (FOV) of the radiometer increases. However, a significant fraction of the data from these channels departs from this linear relationship, reflecting the nonuniform rain that is convective vs. the relatively light stratiform rain. It is inferred from the SSM/I data, in a MCS, when the slope dT sub 3/dT sub 19 is greater than unity there are optically thin clouds which produce light uniform rain. On the other hand, when dT sub 3/dT sub 19 is close to unity, the rain cells have an open structure and correspond to the convective type of rain. The openings between the cells are apparently a result of the downdrafts and/or entrainment. Relatively low values of 85-GHz brightness temperatures that are present when dT sub 37/dT sub 19 is close to unity support these views and, in addition, leads us to conclude that when the convection is heavy this brightness temperature decreases due to scattering by hydrometeors. On the basis of this explanation of the SSM/I data, an empirical rain retrieval algorithm is developed. Radar backscatter observations over the Atlantic Ocean next to Florida are used to demonstrate the applicability of this method. Three monthly mean maps of rainfall over the oceans from 50 degrees N to 50 degrees S, are presented to illustrate the ability of this method to sense seasonal and interannual variations of rain.

Prabhakara, C.↗

Rainfall estimation over oceans from SMMR and SSM/I microwave data

The brightness temperature (T sub b) measured at 37 GHz shows fairly strong emission from rain, and only slight effects caused by scattering by ice above the rain clouds. At frequencies below 37 GHz, where the FOV is larger and the volume extinction coefficient is weaker, it is found that the observations do not yield appreciable additional information about rain. At 85 GHz (FOV = 15 km), where the volume extinction is considerably larger, direct information about rain below the clouds is usually masked. Based on the above idea, 37 GHz observations with a 30 km FOV from SMMR and SSM/I are selected to develop an empirical method for the estimation of rain rate. In this method, the statistics of the observed T sub b's at 37 GHz in a rain storm are related to the rain rate statistics in that storm. The underestimation of rain rate, arising from the inability of the radiometer to respond sensitively to rain rate above a given threshold is rectified in this technique with the aid of two parameters that depend on the total water vapor content in the atmosphere. The retrieved rain rates compare favorably with radar observations and monthly mean global maps of rain derived from this technique over the oceans.

Prabhakara, C.↗

Rainfall estimation over oceans from scanning multichannel microwave radiometer and special sensor microwave/imager microwave data

The brightness temperature (T sub b) measured at 37 GHz shows fairly strong emission from rain, and only slight effects caused by scattering by ice above the rain clouds. At frequencies below 37 GHz, were the fov is larger and the volume extinction coefficient is weaker, it is found that the observations do not yield appreciable additional information about rain. At 85 GHz (fov = 15 km), where the volume extinction is considerably larger, direct information about rain below the clouds is usually masked. Based on the above ideas, 37 GHz observations with a 30 km fov from SMMR and SSM/I are selected to develop an empirical method for the estimation of rain rate. In this method, the statistics of the observed T sub b's at 37 GHz in a rain storm are related to the rain rate statistics in that storm. The underestimation of rain rate, arising from the inability of the radiometer to respond sensitively to rain rate above a given threshold, is rectified in this technique with the aid of two parameters that depend on the total water vapor content in the atmosphere. The retrieved rain rates compare favorably with radar observations and monthly mean global maps of rain derived from this technique over the oceans.

Prabhakara, C.↗

Turbulence spectra of the FIRE stratocumulus-topped boundary layers

There are at least four physical phenomena which contribute to the FIRE boundary layer turbulence spectra: boundary layer spanning eddies resulting from buoyant and mechanical production of turbulent kinetic energy (the microscale subrange); inertial subrange turbulence which cascades this energy to smaller scales; quasi-two dimensional mesoscale variations; and gravity waves. The relative contributions of these four phenomena to the spectra depend on the altitude of observation and variable involved (vertical velocity, temperature and moisture spectra are discussed). The physical origins of these variations in relative contribution are discussed. As expected from the theory (Kaimal et al., 1976), mixed layer scaling of the spectra (i.e., nondimensionalizing wavelength by Z(sub i) and spectral density by Z(sub i) and the dissipation rates) is successful for the microscale subrange and inertial subrange but not for the mesoscale subrange. The most striking feature of the normalized vertical velocity spectra is the lack of any significant mesoscale contribution. The spectral peak results from buoyant and mechanical production on scales similar to the boundary layer depth. The decrease in spectral density at larger scales results from the suppression of vertical velocity perturbations with large horizontal scales by the shallowness of the atmosphere. The spectral density also decreases towards smaller scales following the well known inertial subrange slope. There is a significant variation in the shape of the normalized spectra with height.

Young, G. S.↗