Unlike traditional geostationary estimation, passive microwave estimation using DMSP SSM/I and TRMM TMI is much more direct and accurate. This is because passive microwave rain rates are based on channels that sense precipitation in clouds and do not rely on cloud top temperature. However, the problem with passive microwave rates is that they are extremely sparse in space and time. It is therefore unlikely that a user needing a current estimate of rain over a particular region could obtain a timely passive microwave rain rate.

The solution is to tune the abundant geostationary IR passes using the sparse but accurate passive microwave rain rates. This is done by co- locating in space and time newly arriving microwave and geostationary data sets for 15-degree latitude/longitude boxes. Using these data within each box, an algorithm periodically derives a new lookup table between infrared brightness temperature and rain rate based on the co-located satellite measurements. An important aspect of this procedure is the derivation of a zero-rain rate temperature. At temperatures greater this threshold, no precipitation is estimated. At lower temperatures, estimates are derived which are inversely proportional to temperature. Different relationships and zero-rain rate temperatures are derived for each box. Thus, rainfall estimates will be based on relationships which vary from place to place, optimizing accuracy overall.

There are a number of relatively minor adjustments to this technique to improve the estimates. Cloud tops that are warming (measured over an hour's time) are assumed to be decaying. Rain rates are adjusted downward accordingly. Also, there are a number of multi-spectral tests applied over GOES data to test for thin cirrus. If thin cirrus is detected, no rain rates are derived. One difficulty of the technique is that rainfall is sometimes estimated poorly in regions with high mountains. Thus, a procedure checks for orographic effects in the corresponding NOGAPS data and adjusts the totals accordingly.

2. The technique is regionally tuned, meaning that estimates will be valid within local rainfall regimes.

3. The output is given both in terms instantaneous rain rate and accumulations over different time scales. The instantaneous rates are useful over oceans because precipitation in the atmosphere can interfere with line-of-sight measurements and communications. The accumulations over land give information about soil moisture and potential flooding.

4. The potential impact of precipitation-bearing storms headed for land (tropical cyclones and frontal systems) can be inferred by using this product.

5. Preliminary results suggest that assimilation of this product into numerical models can greatly improve forecasts.

6. The technique should have longevity since many more passive microwave instruments are planned for space. In addition, new multi-spectral satellites will allow improvements worldwide that are now just in effect for GOES.

2. Areas of cold, but thin, cirrus can be misidentified as precipitation on geostationary infrared images. Often this effect appears on the edges of actual precipitation cells as a "fringe" effect (examples below). Thus, precipitation may be correctly identified by the technique, but the precipitating area is too large.

3. The technique is most effective for tropical convection. Precipitation estimates may suffer in midlatitude frontal systems consisting of stratiform clouds, especially over land.

4. The technique should be used with caution over midlatitude land areas in the colder seasons of the year, especially in particularly cold areas. It should not be used when the surface precipitation type is snow.

5. The technique depends on the amount of current passive microwave data that is available. When relatively few passive microwave instruments are orbiting, the resulting match-ups with the infrared can be based on data that are more than twenty-four hours old, resulting in degraded estimates.

6. The technique usually cannot account for orographic precipitation effects that occur near the surface of the earth, well beneath cloud tops. Windward areas can be analyzed as too dry, and leeward areas can be analyzed as too wet. There are partial corrections in effect to account for this problem.

Fig. 2 shows the GMS IR image at 1831 GMT. This image has the advantage of having few gaps and showing up as often as every half hour. The cold tops often indicate precipitation, but the user cannot reasonably guess how much precipitation.

Fig. 3 shows the GMS precipitation analysis based on the data shown in Fig. 1 and 2. Notice that it tends to place precipitation in regions of colored (cold) cloud tops shown on Fig. 2, the IR image. Notice the convective cell between the Philippines and Vietnam. The red color indicates maximum precipitation rates of about 21 mm/hour. This equates to about an inch an hour. This corresponds to the very low IR temperatures of the cloud tops of this cell on Fig. 2, -80 C (yellow).

Fig. 5 shows a twenty-four geostationary accumulation ending at the same time. Notice that the amounts are greater than in Fig. 4, and the total area covered by precipitation is greater. The greater area is explained by the fact that regions of precipitation change as a function of time. The longer the accumulation period, the greater the area will be that is affected by precipitation during the period.

Fig. 6 shows the twenty-four hour TRMM + SSM/I accumulation during the period. Notice that a few regions were not covered by any passes during the period (small black regions). Since this accumulation was based on relatively few microwave passes, the total size of the area affected by precipitation is not great. Notice that this product picks up the fine detail of precipitation systems.