# Presentation: 174931

## Two examples of the unique science from the A-train

## 1. Hurricanes

- Three unique pieces of information that are only available from the A-Train are used to provide new insights on hurricane intensity
- 1. CloudSat provides a direct measure of the altitude of clouds as part of hurricanes (ztop)
- 2. MODIS provides a measure of the emission temperature from tops of clouds (T0)
- 3. CloudSat provides a view of the internal structures of convection in hurricanes - identifies ‘undilute’ convective cores
- These three pieces of information are combined to provide new, physically based estimates of low-level wind speed. Emanuel theory predicts:
- Vmax = f(ztop, T0,SST)
- This is the first theory-based approach for determining storm intensity from satellites (paper attached)

## Hurricanes (continued)

-       Example of A-train data

- Eye of hurricane

- MODIS 11um
- brightness
- temperature

## Clodsat+MODIS data

- Hurricanes (continued)
-       Using the data to determine intensity

## 2. Making warm rain

- The issue - one of the longstanding, unresolved questions in atmospheric sciences is why does it rain as rapidly as observed in nature. Theory has not been able to answer this baqsic question.
- Why this is important - the warm rain process is elementary to many of the key climate issues related to the hydrological cycle, elementary to the problem of effects of pollution on rain, and central to understanding of essential cloud-climate feedbacks.
- Prior to the A-Train we were only able to study the warm rain production processes in the laboratory or as part of limited area field experiments. Thus we have not been able to probe issues  associated with large-scale environmental influences (like aerosol) on rain production.

## The coalescence process - falling drops grow through collisions with smaller drops

- MODIS provides information on the cloud water,
- Cloudsat reflectivity information provides a way of discriminating he incipient stages of precipitation formation
- For these cases, CloudSat radar provides a view of the the precipitation drops.
- Combined, these observations provide a direct measure of the rate at which precipitation water is created.

- MODIS is largely sensitive to small drops

- CloudSat radar is largely sensitive to these collector drops

## Stephens and Haynes, 2007

- This figure shows the first large-scale estimate of coalescence (y axis) of cloud water (x axis) - the time scale implied is a critical parameter for representing this process in models

- A global view of coalescence

## OMI effective cloud pressure from Raman scattering: UV/visible light penetrates deeper  

- One A-train example: What do clouds look like to OMI, MODIS and Cloudsat? (Important question for trace-gas retrievals)

- MODIS: sensitive to cloud-top
- (not appropriate for UV-VIS trace-gas retrievals)

- OMI effective cloud pressure from Raman scattering: UV/visible light penetrates deeper

- 3. Optimizing NASA’s EO resources:
- CloudSat and the A-Train -  solved a long-standing paradox: differences in cloud-top heights

## Radar/Lidar Combined Product Development

- Formation flying is a key design element in cloudsat
- CloudSat has demonstrated formation flying as a practical observing strategy for EO.
- Overlap of the CloudSat footprint and the CALIPSO footprint, within 15 seconds, is achieved >90% of the time.

## lidar/radar combined ice microphysics - new A-Train ice cloud microphysics

- Preliminary example from Zhien

## MLS

- ECMWF

- CloudSat

- A-train Cloud ice

## Wu et al., 2007

**Comparison to MLS that employs entirely different physics gives much insight on sensitivity and characteristics of both ‘products’**
**Statistically, cloudsat and MLS closely agree.  **

## A case study example

- of comparison between CloudSat and AMSRE -
- passive microwave methods are missing significant fractions of light precipitation - this exposes issue with precipitation algorithms

- The portion of orbit highlighted by red is where Cloudsat determines precipitation to have occurred. AMSRE considers preciptation to occur only when the cloud liquid water reaches some (arbitrary and untested) threshold)

## 2. Comparison of precip frequency

- PIA-based
- precip

- There are major differences in the two precipitation estimates that are just now beginning to be understood