## 1. Introduction

[2] It is well known that it is difficult to assess rain rates over the global tropics. However, the advent of satellite observing system and the advancement of retrieval algorithms have partially solved this problem. As an extension of the previous companion superensemble (SE) studies of deterministic precipitation forecasts (part 1 [*Shin and Krishnamurti*, 2003]), the probability of precipitation (POP) forecast will be investigated using satellite-based rain products. A probabilistic SE approach will be proposed in the present paper as well.

[3] While the ensemble mean represents a consensus deterministic forecast, the distribution of forecasts in an ensemble is most useful for generating probabilistic forecasts. Probabilistic predictions differ from the deterministic forecasts in that, depending on the expected likelihood of forecast events, they assign a probability value between 0 and 1, instead of exclusively using no (0) and yes (1) as forecast outcomes. Most forecasts are associated with uncertainty and the level of uncertainty is situation dependent. The use of probabilities allows the generator of a forecast to explicitly express the level of uncertainty associated with a given forecast.

[4] A probabilistic forecast is one that estimates the probability of occurrence of a chosen event *E*. The event type selected for this study is the “precipitation exceeding a pre-defined threshold level.” The probability of a forecast event from an ensemble system at a fixed point is based on the fraction of ensemble members predicting that event. For an ensemble of equally reliable models, the probability of the event *E* is (*n*/*N*) × 100%, where *n* is the number of ensemble members forecasting *E*, and *N* is the total number of ensemble forecasts.

[5] In recent years, there have been several studies in the field of probabilistic precipitation forecasts which extensively make use of ensemble prediction systems [e.g., *Du et al.*, 1997; *Hamill and Colucci*, 1998; *Eckel and Walters*, 1998; *Buizza et al.*, 1999; *McBride and Ebert*, 2000; *Mullen and Buizza*, 2001]. They all have shown that the ensemble forecast provides a more accurate rainfall forecast than a single model forecast.

[6] Utilizing the ensemble prediction system, *Krishnamurti et al.* [2000, 2001] made a cornerstone for precipitation forecasts from an optimal ensemble forecast produced by combining individual forecasts from a group of models with help of the pre-computed optimal statistics, that is, a SE forecast. They showed that the deterministic SE precipitation forecasts for days 1, 2, and 3 are invariably superior to various conventional forecasts. However, they did not consider the probabilistic aspects of their SE forecasts. This portion of SE forecasts will be covered in this study. Since the basic notion of the SE is that not all models are equally reliable in different points in space, it is to be expected that the probability associated with the resulting deterministic forecasts would not be the same for the SE and the regular ensemble. After suitably defining the corresponding probability from using the SE method, it is desirable to compare the relative qualities of the two probabilistic forecasts.

[7] The present paper is devoted to the application of the SE approach to POP forecasts. Section 2 deals with the various satellite rain rate algorithms and physical initialization. After discussing ensemble members devised in this study in section 3, probabilistic superensemble techniques are introduced in section 4. Results of the probabilistic precipitation forecasts are then analyzed in section 5. Conclusions follow in section 6.