2000 Volume 78 Issue 4 Pages 333-355
Latent heating profiles associated with three TOGA COARE active convective episodes (December 10-17 1992; December 19-27 1992; and February 9-13 1993) are examined using the two-dimensional version of the Goddard Cumulus Ensemble (GCE) Model, and retrieved by using the Goddard Convective and Stratiform Heating (CSH) algorithm. The following sources of rainfall information are input into the CSH algorithm: Special Sensor Microwave Imager (SSM/I), shipborne radars and the GCE model. Diagnostically determined latent heating profiles are calculated using 6 hourly soundings used for validation.
The GCE model simulated rainfall and latent heating profiles are in excellent agreement with those estimated by soundings. In addition, the typical convective and stratiform heating structures (or shapes) are well captured by the GCE model. Radar measured rainfall is smaller than that estimated by the GCE model and SSM/I in both December convective episodes. SSM/I derived rainfall is more than the GCE model simulated for the December 19-27 and February 9-13 periods, but it is in excellent agreement with the GCE model for the December 10-17 period. The GCE model estimated stratiform amount is about 50% for December 19-27, 42% for December 11-17 and 56% for the February 9-13 case. These results are consistent with large-scale analyses. Accurate estimates of stratiform amount are needed for good latent heating retrieval. A higher (lower) percentage of stratiform rain can imply a maximum heating rate at a higher (lower) altitude. The GCE model always simulates more stratiform rain (10 to 20%) than the radar for all three convective episodes. The SSM/I derived stratiform amount is about 37% for December 19-27, 48% for December 11-17 and 41% for the February 9-13 case.
Temporal variability of CSH algorithm retrieved latent heating profiles using either the GCE model simulated or radar estimated rainfall and stratiform amount is in good agreement with that diagnostically determined for all three periods. However, less rainfall and a smaller stratiform percentage estimated by radar resulted in a weaker (underestimated) latent heating profile, and a lower maximum latent heating level compared to those determined diagnostically. Rainfall information from SSM/I can not retrieve individual convective events due to poor temporal sampling. Nevertheless, this study suggests that a good rainfall retrieval from SSM/I for a convective event can lead to a good latent heating retrieval.
Sensitivity testing has been performed and the results indicate that the SSM/I derived time averaged stratiform amount may be underestimated for December 19-27. Time averaged heating profiles derived from SSM/I, however, agree well with those derived by soundings for the December 10-17 convective period. The heating retrievals may be more accurate for longer time scales, provided there is no bias in the sampling.
An appropriate selection of latent heating profiles from the CSH look-up table is important. Sensitivity tests addressing this issue have been performed.
