# Presentation: 462048

## JCSDA 2006 – GSD Review

- Dan Birkenheuer
**Visions for the future, ancillary work that ties into this year’s proposal**

## INTRODUCTION

- Still working on funding for this year
- We still maintain our general objectives but the lack of funding has essentially “slowed” things down
- Presentation will focus on:
- GSI
- Cal Val using GPS data in moisture retrievals

## Working with GOES

- GSI will be a part of the LAPS system by the end of this month, code to be tested in RUC as well
- Plan will be to start the implementation of gradient radiance assimilation and testing after that time.
- Eventual plan will be to enable the use of AIRS radiances for this as a proxy for GOES R
- GPS IPW data is being used to validate GOES moisture products
- Examples of this will be shown
- Plan to acquire AIRS moisture products for similar real-time validation as a proxy for GOES R products

## RADIANCE GRADIENTS

- Shown to work with current GOES in LAPS
- Product data (GOES 10&12 routine, AIRS next)
- Next step is to reformulate radiance assimilation term to use gradients for LAPS GSI
- Code should transfer to RUC and eventual inroads to operations after checkout with case studies etc.
- Publications and posters
- Several publications are now available addressing this approach:http://laps.fsl.noaa.gov/cgi/birk.pubs.cgi

## Gradient assimilation test

- Side by side comparison of LAPS moisture product using both the old and new gradient methods.
- New method now running routinely in LAPS and providing superior performance.
- QC could be improved

## Exploiting the GOES product gradient field for structure and reduced bias

**Exploiting the GOES product gradient field for structure and reduced bias**

## Left the old method, right the new method with associated power spectra

## CAL VAL with GPS IPW

- Using GPS IPW data to compare with GOES product data
- Working with UW Madison and NESDIS
- Tim Schmit, Gary Wade, Jim Nelson
- Jaime Daniels
- Making progress, looking at outliers

## Recent observations denoting breakdown in quality around 1 March 2006

## Ability to ID outliers

**sat12:1.6600:PCLA0607714: 30.4690: -87.1894: 1.34:  3.00 **** **
**sat12:1.6850:TXCC0606621: 27.7408: -97.4417: 2.48:  4.17 **** **
**sat12:1.7050:MOB10607714: 30.2275: -88.0241: 1.79:  3.49 **** **
**sat12:1.7600:MIA30607521: 25.7328: -80.1602: 1.66:  3.42 **** **
**sat12:**_**1.8750**_**:TXPR****0607802****: ****26.2085: -98.1893****: ****3.31:  5.19**

## Here is a definite outlier that really defies current explanation.  The site was tracking well up until 07 UTC when for some reason the solution went way dry.  Other solutions see to follow the GPS and RAOB quite well.

## Not always does a high bias indicate a bias.  Here is a high bias that shows a slight 

- Not always does a high bias indicate a bias.  Here is a high bias that shows a slight
- Mis-match in data time can have a drastic affect on statistics.

- Perceived bias

## RAOB & GPS

- Here is the time series over Platteville CO with the Denver RAOB data, the arrows point out the outliers in both GOES 12 and 10, and 12 is much greater.  Note this is a very dry situation so the relative differences are quite large.

- 10

- 12

## A start at collaboration with UW Madison on the retrieval problems.  Here we see that the first guess that went into the CIMSS Legacy retrieval has a better fit to the microwave data than the GOES 12 retrieval that is moist biased.

## You look at this and can’t stop to wonder “what is going right in Ohio, PA and New England, and not so good in TX?”  It is drier in the NW and much moister in TX.

## Summary

- Objectives have not changed but slowed down.
- Using LAPS to prototype GSI efforts
- Continuing to use GPS for Cal Val
- Errors in moisture retrievals could be useful for examining problems in thermal retrievals too (GOES, AIRS, GOES R)
- Law of Unintended Consequences
- GPS system up for termination this FY
- GSD going global