# Presentation: 511261

## On the Effects of Warm Rain Clouds in the Tropics

*Anita D. Rapp, Chris Kummerow, and Gregory Elsaesser*
*6 February 2008*

*Third TRMM NASA/JAXA International Science Conference*

## Motivation

- Recent studies have shown that warm rain systems in the Tropics may be sensitive to the underlying SST
- There have been many studies tying cloud feedbacks to SST, but most focus on deep convection and anvil cirrus production

## Why are warm rain systems important to the climate system?

- Warm rain systems are prevalent in the Tropics and contribute to the total tropical rainfall
- They affect the radiation budget, mostly through their interaction with shortwave radiation
- In the absence of other changes, a reduction in warm rain cloud amount would allow more solar radiation to reach the surface
- Also affect the hydrologic cycle through their moistening of the lower troposphere which preconditions the atmosphere for deep convection
- A reduction in the amount of evaporating warm rain cloud could alter the recycling timescales for deep convection

## Are these systems responding to SST?

- Original idea was to look at retrievals from a variety of sensors and algorithms to see how warm rain systems respond to SST.

- No rain

- Rain

## What to do?

- Modify microwave optimal estimation algorithm to -
- Ingest deconvolved microwave Tbs
- Take into account partially cloud-filled pixels
- Calculate emission/scattering for raining pixels

- 30° S – 30° N, 130° E – 170° W

- Data -
- TMI brightness temperatures
- 10,19, 37, 21, 85 GHz Tbs
- VIRS cloud fraction
- PR precipitable water and rain height
- RSS TMI-derived SST

## TMI Deconvolution

- Sharpened
- coast lines

- Enhanced cloud
- scale features

- Used to place all microwave channels on a common spatial resolution
- Method utilizes footprint overlap between adjacent pixels
- Modifies footprint resolution while minimizing error propagation

## Basic OE Algorithm

**Establish Convergence Criteria**
**Retrieval**
**(Determine Validity of Retrieval)**

**Ingest Observed Brightness Temperatures (TBs)**

**Prior Knowledge of Atmospheric State **
**     ****(Water Vapor, Wind, Cloud Water)**

**Adjust Water Vapor, **
**Wind, Cloud Water**

**Minimize Cost Function:**

***Iterate***

**Simulate TBs**

## Modifications to OE Algorithm

- New inputs -
-  Ingest deconvolved TMI Tbs
-  VIRS cloud fraction calculated within TMI footprint
-  Specify the rain column from PR-derived rain water and rain height

*TB**CLD*  includes scattering and extinction calculated from PR rain water

- VIRS Cloud fraction within TMI footprint

- Modify cost function -

## Results

## Results – Difference 

- Results – Difference

-   Deconvolution tends to reduce LWP
-   Largest difference is at lower cloud fractions
- - cloud fraction <1 tends to increase LWP

## Results w/ SST

- No rain

- Rain

## Testing Influence on Deep Convective Time Scales

- MJO identified by numerous studies in April – May 2001

- Study time series of surface, atmospheric and cloud properties
- Examine the influence of properties of warm rain systems on lower tropospheric moistening

## Results

## Results

## Summary

- When effects of resolution, partially cloudy pixels, and rainfall are taken into account, visible and microwave sensor retrievals show better agreement
- Results indicate a change in the behavior of cloud properties from non-raining to raining clouds
- LWP changes little with SST for non-raining clouds, but strongly decreases with SST in warm rain clouds
- Microwave retrieval error diagnostics indicate that deconvolution and cloud fraction information generally improves the retrieval, but very low cloud fraction and rainfall information can influence it in either direction
- MJO case study suggests that warm rain systems are correlated to moistening in low - mid levels

## Future Work

- Study more MJO cases to try to understand link between properties of warm rain clouds and lower tropospheric moistening and their influence on the recycling time scales of deep convection
- Test and refine assumptions about DSD in retrieval algorithm
- Examine global distribution of warm rain cloud properties and try to understand regional differences
- Finish PhD
- Get a job – Hint, hint....