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Relationships among lightning, precipitation, and hydrometeor characteristics over the North Pacific Ocean

This page is part of a paper:
Pessi, A., and S. Businger, 2009: Relationships Among Lightning, Precipitation, and Hydrometeor Characteristics over the North Pacific Ocean.
Journal of Applied Meteorology and Climatology, 48, 833-848.

Background

Accurate knowledge of the distribution and evolution of moisture- and latent heating fields associated with deep convection is essential for accurate numerical forecasts of cyclogenesis (e.g., Anthes et al. 1983; Brennan and Lackmann 2005). The paucity of in situ observations over the North Pacific Ocean can lead to significant errors in the initial moisture fields input into operational numerical models. These observational errors in turn often lead to large forecast errors in simulated storm geopotential height, wind, and rainfall (e.g., McMurdie and Mass 2004).

A promising and important application of PacNet is to derive estimates of the rainfall rate and latent heating and hydrometeor profiles from lightning data over the Pacific Ocean that can be assimilated into numerical weather prediction (NWP) models. In areas of an NWP model domain for which the moisture content is underestimated, lightning data can contribute to forecast accuracy by adjusting the moisture fields and vertical profiles of latent heat release into the initial conditions and early forecast hours of the model in areas where lightning is observed (Alexander et al. 1999; Chang et al. 2001).

Methodology

Lightning data from the Pacific Lightning Detection Network (PacNet) and Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission (TRMM) satellite were compared to TRMM precipitation radar products and latent heating and hydrometeor data. Three years of data over the central North Pacific Ocean (0-40N; 140-180W) were analyzed. The data were divided into winter (October-April) and summer (June-September) seasons. Summer thunderstorms were triggered by cold upper-level lows associated with the tropical upper-tropospheric trough (TUTT) (Fig. 1, left). During the winter, the thunderstorms were typically embedded in cold fronts associated with eastward propagating extratropical cyclones (Fig. 1, right). Concurrent lightning and satellite data associated with the storms were averaged over 0.5 x 0.5 degree grid cells and a detection efficiency correction model was applied to quantify the lightning rates.

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Figure 1. (Left panel) A typical summer storm. (a) GOES-10 infrared image at 1130 UTC on 30 August 2005 overlaid with one-hour of PacNet lightning observations. (b) NCEP Reanalysis at 1200 UTC on 30 August 2005 at 250 hPa shows a low northwest of Hawaii. (c) NWS subjective surface analysis of the central Pacific at 1200 UTC on 30 August 2005 shows a surface trough northwest of Hawaii. Streamlines are plotted in gray south of 30N. (Right panel) As in left panel, but for 0600 UTC on 12 December 2005. Satellite image in (a) is valid at 0630 UTC.


Summary of the results

The results of the data analysis show a consistent logarithmic increase in convective rainfall rate with increasing lightning rates (Fig. 2). Moreover, other storm characteristics, such as radar reflectivity (Fig. 3a), storm height (Fig. 3b), ice water path, and latent heat (Fig. 4) show a similar logarithmic increase. Specifically, the reflectivity in the mixed-phase region increased significantly with lightning rate and the lapse rate of Z decreased (Fig. 5); both these features are well-known indicators of the robustness of the cloud electrification process. In addition, the height of the echo tops showed a strong logarithmic correlation with lightning rate. These results have application over data-sparse ocean regions by allowing lightning-rate data to be used as a proxy for related storm properties, which can be assimilated into NWP models.

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Figure 2. (a) Convective rainfall vs. lightning rate and (b) stratiform rainfall vs. lightning rate. Squares and solid lines are for winter data, and diamonds and dashed lines for summer data. Filled symbols are PacNet data, open symbols are LIS data, and grey symbols are combined PacNet and LIS data. Abscissa shows the number of lightning flashes per hour normalized over 10,000 km2. The error bars are ± 1 stdev.


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Figure 3. (a) Maximum PR reflectivity and (b) maximum height of radar echo. Lines and symbols as in Fig. 2.


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Figure 4. Vertical profiles of latent heating in (a) winter and (b) summer for low, moderate, and high lightning rates. (c) Maximum latent heating rate vs. lightning rate.


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Figure 5. Vertical profiles of PR reflectivity in (a) winter and (b) summer for three different lightning rate categories (low, moderate, and high lightning rate).


Acknowledgments: We are grateful to Ken Cummins and Nick Demetriades for their support in the development of PacNet and for providing PacNet data, to Joseph Nowak for help with detector site selection and installation, and to Nancy Hulbirt for assistance with graphics. TRMM 2A12 and 2A25 data products were obtained from NASA Goddard Space Flight Center DAAC and LIS data from Global Hydrology Resource Center. This work is supported by the Office of Naval Research under grant numbers N00014-08-1-0450 and N00014-05-1-0551.

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