The effect of assimilating rain rates derived from satellites and lightning on forecasts of the 1993 superstorm

• Published in: Monthly weather review 1999-07, Vol.127 (7), p.1433-1457
• Main Authors: ALEXANDER, G. D, WEINMAN, J. A, KARYAMPUDI, V. M, OLSON, W. S, LEE, A. C. L
• Format: Article
• Language: English
• Subjects: Earth, ocean, space; Exact sciences and technology; External geophysics; Geophysics. Techniques, methods, instrumentation and models
• ISSN: 0027-0644; 1520-0493
• DOI: 10.1175/1520-0493(1999)127<1433:TEOARR>2.0.CO;2

Inadequate specification of divergence and moisture in the initial conditions of numerical models results in the well-documented ``spinup'' problem. Observational studies indicate that latent heat release can be a key ingredient in the intensification of extratropical cyclones. As a result, the assimilation of rain rates during the early stages of a numerical simulation results in improved forecasts of the intensity and precipitation patterns associated with extratropical cyclones. It is challenging, however, particularly over data-sparse regions, to obtain complete and reliable estimates of instantaneous rain rate. Here, a technique is described in which data from a variety of sources--passive microwave sensors, infrared sensors, and lightning flash observations--along with a classic image processing technique (digital image morphing) are combined to yield a continuous time series of rain rates, which may then be assimilated into a mesoscale model. The technique is tested on simulations of the notorious 1993 Superstorm. In this case, a fortuitous confluence of several factors--rapid cyclogenesis over an oceanic region, the occurrence of this cyclogenesis at a time inconveniently placed in between Special Sensor Microwave/Imager overpasses, intense lightning during this time, and a poor forecast in the control simulation--leads to a dramatic improvement in forecasts of precipitation patterns, sea level pressure fields, and geopotential height fields when information from all of the sources is combined to determine the rain rates. Lightning data, in particular, has a greater positive impact on the forecasts than the other data sources.