# Presentation: 238697

## Ground Validation for GPM Microwave Imager 

- <date/time>

**Ground Validation for GPM Microwave Imager **
**Algorithm Development**

**Bill Olson, Mircea Grecu, Wei-Kuo Tao, Chung-Lin Shie, Steve Lang, **
**Wes Berg, Chris Kummerow, and Tristan l’Ecuyer   **

**TB     **

**Notes:**
GVS is Ground Validation Segment

## TMI Algorithm Database Constructed from 

- <date/time>

**TMI Algorithm Database Constructed from **
**                    ****PR/TMI Estimates**

- TMI

- PR

**reflectivity**
**profiles**

-     TMI
- footprints

**retrieved**
**precipitation**
**profile**

## PR/TMI-based  TMI Estimation Algorithm

**database of**
**precipitation**
**profiles**
**   ****~ 10****6**

**prob (R****1****)**

**prob (R****3****)**

**prob (R****2****)**

**• • • • • •**

**weighting    ~**     **prob (R****i****)**

**Each estimate is a weighted average of database profiles:**

## PR/TMI-based  TMI Estimation Algorithm

**database of**
**precipitation**
**profiles**

******   ****prob { TB(R****i****) |  TB****observed  ****}**

**TB(R****1****)**

**TB(R****2****)**

**TB(R****3****)**

**• • • • • •**

**weighting    ~**     **prob (R****i****)**

## PR/TMI-based  TMI Estimation Algorithm

**database of**
**precipitation**
**profiles**

**weighting    ~**     **prob (R****i****)**

**“****statisical”**

**“****physical”**

******   ****prob { TB(R****i****) |  TB****observed  ****}**

## Algorithm Validation and Improvement

- <date/time>

**Algorithm Validation and Improvement**

**investigate with **
**GV ****“precipitation **
**process” sites**** **

**improve **
**forward model**
**and database**
**consistency**

**   ****update **
**algorithm**

## Bias Identification - “Product Validation” Sites

- <date/time>

**Bias Identification - “Product Validation” Sites**

**• ****supplement with US National Network of radars (product**
**  ****validation sites)**

**•  ****use spaceborne**
**   ****radar (DPR) as**
**   ****reference**

## Algorithm Diagnostics

- <date/time>

**Algorithm Diagnostics**

**•  ****evaluate forward model physics**
**	****-  precipitation size distributions**
**	****-  non-precipitating cloud**
**	****-  EM properties; particularly ice & mixed phase**
**	****-  radiative transfer**
**•  ****evaluate regime-dependent precipitation**
**   ****statistical characteristics**
**	****-  relative number of system types**
**	****-  precipitation geometry**
**	****-  factors affecting precipitation efficiency**

## Forward Model Physics- 

- <date/time>

**Forward Model Physics- **
**Aircraft Studies at “Precipitation Process” Site**

**•  ****utilize multi-channel active and passive sensor observations of **
**   “****narrow beam”**

**•  ****active:  10 - 89 GHz;  passive: 10 - 300 GHz**

**•  ****find forward model physical parameterizations that are**
**   ****consistent with ****all**** ****observations**

## Airborne Cloud Radar and Microwave Radiometer 

- <date/time>

**Airborne Cloud Radar and Microwave Radiometer **

**Wakasa Bay **
_**Field Campaign**_

**On NASA P3 Aircraft:**
**• ****Airborne Cloud Radar**
**(****94 GHz****)**
**• ****Precip Radar-2 (14, 35 GHz)**
**• ****Polarimetric **
**  ****Scanning **
**  ****Radiometer**
**  ****(10.7, 18.7, ****37.0, 89.0 GHz****)**
**• ****Millimeter-wave Imaging**
**   ****Radiometer (90 - 340 GHz)**
**• ****AMMR (21, 37 GHz  - **
**  ****upward looking)**

**Notes:**
Wakasa Bay Field Campaign was designed to validate light precipitation (including snow) retrievals of the AMSR-E instrument on Aqua.  It took place January-February 2003 over the Sea of Japan.

## Forward Model Physics- 

- <date/time>

**Forward Model Physics- **
**GMI Simulator at “Precipitation Process” Sites**

## Flow Diagram

- <date/time>

**Simulator**

**Flow Diagram**

**   ****Convolve Radiances with**
**   ****GMI Response Functions;**
**               ****Add Noise**

**Notes:**
GMI is GPM Microwave Imager

## Prototype GMI Simulator

- <date/time>

**Prototype GMI Simulator**

## Forward Model Parameterizations- 

- <date/time>

**Forward Model Parameterizations- **
**DPR as Roving “Precipitation Process” Site**

##          Precipitation Statistical Characteristics -

- <date/time>

**         ****Precipitation Statistical Characteristics -**
**  ****“****Bias-Free” TMI Algorithm Applied to July 2000 Data**

## Precipitation Statistical Characteristics -

- <date/time>

**Precipitation Statistical Characteristics -**
**               ****Long-Term Monitoring**

**• ****use DPR plus ancillary**
**  ****data to identify regime-**
**  ****dependent biases.**
**• ****direct “product validation” **
**  ****sites and “precipitation **
**  ****process” sites can also play **
**  ****a role.**
**•  ****what sub-populations**
**   ****of rain system types,**
**   ****precipitation structures,**
**   ****etc., occur more**
**   ****frequently in a given**
**   ****regime?**

**July 2000**

**January 1998**

## Latent Heating

**database of**
**precipitation**
**profiles**

**weighting    ~**     **prob (****LH****i****)**

**Each estimate is a weighted average of database profiles:**

******   ****prob { TB(****LH****i****) |  TB****observed  ****}**

**LH**

## Validation of Cloud-Resolving Models- 

- <date/time>

**Validation of Cloud-Resolving Models- **
** ****Measure Large-Scale Forcing at “Process” Sites**

**• ****drive CRM’s with **
**  ****measured advective**
**  ****tendencies**

**• ****simulate GV radar and**
**  ****satellite observations;**
**  ****are observations **
**  ****reproduced in a statistical**
**  ****sense?**

## Q1,  Q2, 

- <date/time>

** **

**SCSMEX Rawinsonde Network and Polygons**

**for NESA:**
***Q******1******,  Q******2******,****** ***
***<Q******2******>*****  =  *****L******v***** (*****P******Rawin***** - *****E*****)**
- Johnson and Ciesielski,
- (2002; person. comm.)

## SCSMEX Obs vs. GCE-  2241 UTC 20 May 1998

- <date/time>

**SCSMEX Obs vs. GCE-  2241 UTC 20 May 1998**

**Notes:**
So we took a long-term simulation of convection during the SCSMEX field campaign to compare the simulated normalized polarization and scattering indices and the corresponding observed indices during TRMM overpasses.  These pictures are from an overpass that occurred during the monsoon onset period in the SCS, the observed and modeled normalized polarizations at 19 GHz in the top panels and the observed and modeled scattering indices in the lower panel.  We used 72 overpasses of the SCSMEX NESA for our analysis; however, this snapshot illustrates some basic differences between the observations and model:  There tends to be a greater level of convective organization in the observed precipitation, resulting in lower 19 GHz NP generally.  Also at 85 GHz, it is apparent that the modeled convection is intense but “local”, with not much stratiform precipitation development.  SI values are generally higher than those observed. 

## GCE Model vs. TMI Observations

- <date/time>

**GCE Model vs. TMI Observations**
**(SCSMEX 30-Day Period)**

**-histograms of observed and modeled 19 GHz rain fraction**
** ****index vs. 85.5 GHz scattering index.**

**Notes:**
This is the same as previous slide, but for model simulations with modified collection, in which graupel does not collect snow.  It has been noted that the standard GCE microphysics allows excessive graupel growth in stratiform precipitation areas due to collection of snow.  In the 30-day simulation that’s represented here, the collection process is turned off, resulting in less graupel (and therefore less intense scattering at 85 GHz and lower SI’s).  The impact of this change is relatively small, and it does not greatly enhance convective organization; i.e., greater stratiform rain development, with lower 19 GHz NP’s.  So we still have work to do on the CRM simulations-  we need to figure out why the simulation produce so much ice without developing much stratiform rain, in particular.  Is it microphysics, dynamics?  We don’t have an answer yet.

## Latent Heating Estimation-  PR/TMI Database

- <date/time>

**Latent Heating Estimation-  PR/TMI Database**

**(using PR/TMI Database)**

***Johnson and Ciesielski (2002)***

## Concluding Remarks

- <date/time>

**Concluding Remarks**

**• ****identify regime-dependent biases with**
**	****-  GMI vs DPR/GMI difference maps**
**	****-  enhanced national radar network**
**• ****validate physics of algorithm using**
**	****- multi-sensor aircraft observations**
**	****- radar-driven GMI “simulator”**
**• ****validate statistics of rain system structure using**
**	****- DPR/GMI observed distributions**
**	****- enhanced national radar network**
**• ****latent heating**
**	****- validate cloud-resolving models**
**	****- no global reference currently**

## Regime-Dependent Precipitation Characteristics -

- <date/time>

**Regime-Dependent Precipitation Characteristics -**
**                     ****Long-Term Monitoring**

**• ****direct “product validation” sites and “precipitation **
**   ****process” sites can also play a role**

**•  ****removal of rain bias using column water vapor:**

## Latent Heating Profile Assignment to Database

- <date/time>

**Latent Heating Profile Assignment to Database**

**Need ****cloud-resolving models**** to associate heating profiles.**