# Presentation: 271253

## Global Applications of Remotely-Sensed Latent Heating Distributions from a PR-Trained Radiometer Algorithm

**Global Applications of Remotely-Sensed Latent Heating ****Distributions from a PR-Trained **_**Radiometer**_** Algorithm**

**• ****Intro**
**• ****Method**
**• ****Comparisons of Radiometer Estimates to Training**
**   ****Data and Rawinsonde Estimates**
**• ****Estimating Q****1**
**• ****Applications**

## Why use microwave radiometer data?

**•  ****TMI sampling error for monthly mean ****	****   **
**   ****rain rate of 10 mm day****-1 ****over a **
**   ****2.5****o**** x 2.5****o**** box is about 25%.**
**•  ****Same applies to latent heating.**
**• ****TMI sampling error is roughly 1/sqrt(3)  **
**  ****or 58% of the PR sampling error.**
**•  ****Two radiometers would yield 1/sqrt(6)**
**   ****or  41% of the PR sampling error.**

***from Bell and Kundu, NASA/GSFC***

## What is it we’re really trying to estimate?

**Generally, we estimate *****Q*****1**** - *****Q*****R ****.... **

***from Wei-Kuo Tao***

## How do we “train” the TMI algorithm with PR data?

_**First Step**_** -  Create Training Data:**

**echo top**

**surface **
**rain rate**

**conv/strat**
**(from 2A23)**

## How do we “train” the TMI algorithm with PR data?

_**Second Step**_** -  Retrieve Heating from TMI using **
**	****	****    ****PR-derived Database:**

**       ****Large Database of **
**PR-Derived Heating Profiles **
**                   ****and**
**       ****Collocated TMI *****TB*****’s**
**          ****(from First Step)**

**Bayesian Estimation **
**           ****Method**

**•  ****So we have two heating algorithms: PR and TMI.**
**•  ****TMI algorithm tries to imitate the PR’s behavior.**

## How consistent are TMI and PR heating estimates?

***How consistent are TMI and PR heating estimates?***

**Time Series Mean *****Q*****1****-*****Q*****R**

***from Mircea Grecu***

## How consistent are TMI and PR heating estimates?

**October 1998 Mean Heating Estimates**

## Comparisons to Independent Observations

**•  ****Add radiative heating (*****Q*****R****) from**
**   ****Tristan L’Ecuyer to *****Q*****1****-*****Q*****R**** from**
**   ****TMI algorithm to obtain *****Q*****1****.**
**•  ****Some differences due to limited **
**   ****sampling by TMI (~ 1.3 day****-1****).**
**•  ****Some deficiency in deep    **
**   ****stratiform heating.**

## Comparison to ECMWF Mean Heating in the Tropics from Xianan Jiang

**•  ****Equatorial (10****o****S - 10****o****N)   **
**   ****10/98 - 3/99 mean heating **
**   ****and rain rates.**
**•  ****Add *****Q*****1****-*****Q*****R**** from trained**
**   ****TMI algorithm to *****Q*****R**** from**
**   ****Tristan L’Ecuyer’s algorithm**
**   ****to obtain *****Q*****1****.**
**•  ****Overall, see greater heating**
**   ****rates from the ECMWF model**
**   ****than observed.**
**•  ****The model heating excess **
**   ****appears to be associated with **
**   ****greater rain rates from the**
**   ****model. **

**Notes:**
Time series of Q1 in 20 x 20 deg box in the central, equatorial Indian Ocean uses a 5-day running mean.  Note variability of heating structure related to variations in the percentages of stratiform rainfall.  These variations of heating are not represented in the model.

## Intraseasonal Variability from TRMM Microwave Imager   from Xianan Jiang

**• ****Time series of heating and    **
**  ****stratiform rain percentage in **
**  ****box 10****o****S - 10****o****N, 70****o****E - 90****o****E**
**  ****from 10/98 - 3/99:**
**• ****See intraseasonal variations in**
**  ****observed *****Q*****1****, but not in the**
**  ****model.**
**• ****Stratiform rain percentage **
**  ****shows similar behavior.**

**Notes:**
Time series of Q1 in 20 x 20 deg box in the central, equatorial Indian Ocean uses a 5-day running mean.  Note variability of heating structure related to variations in the percentages of stratiform rainfall.  These variations of heating are not represented in the model.

## Outlook

**•  ****After much struggle, finally we’re starting to obtain**
**   ****credible estimates from both PR and TMI.**
**•  ****Need to diversify the cloud-model generated heating**
**   ****look-up tables for range of environmental forcings.**
**•  ****Main challenge is obtaining credible error estimates **
**   ****using independent heating data, due to the lack of **
**   ****such data and low sampling rate by satellite.**

## Convective Heating Modes

**•  ****Decompose the mean**
**   ****retrieved precipitation**
**   ****liquid water content and**
**   ****convective heating profiles**
**   ****by precip top height.**
**•  ****Plot shows 3 possible **
**   ****convective heating modes:   **
**   ****shallow, “congestus”, and **
**   ****deep.**

**Notes:**
Time series of Q1 in 20 x 20 deg box in the central, equatorial Indian Ocean uses a 5-day running mean.  Note variability of heating structure related to variations in the percentages of stratiform rainfall.  These variations of heating are not represented in the model.

## How consistent are TMI and PR algorithms with respect to heating-related parameters?     

***How consistent are TMI and PR algorithms with respect to ******heating-related parameters?******     ***

**PR (2A25)**

**TMI**