# Presentation: 138337

## AIRS Profile Evaluation and Assimilation

**Bradley Zavodsky**
**Gary Jedlovec**
**Shih-hung Chou**
**November 21, 2005**

## Motivation for Evaluating AIRS Profiles  
AIRS Science Team has done extensive global validation 
Quantify regional errors in the AIRS soundings (collaborative with OU EOS activity)
Apply knowledge of localized errors to tune parameters for regional data assimilation
High-End Methodology
RMS errors between regional RAOBs and collocated AIRS soundings for January 2004 (supports January 14-17 case study)
Analyze differences between quality indicators in the AIRS V4.0 retrievals

- AIRS Science Team has done extensive _global_ validation
- Quantify _regional_ errors in the AIRS soundings (collaborative with OU EOS activity)
- Apply knowledge of localized errors to tune parameters for regional data assimilation
- High-End Methodology
- RMS errors between regional RAOBs and collocated AIRS soundings for January 2004 (supports January 14-17 case study)
- Analyze differences between quality indicators in the AIRS V4.0 retrievals

## AIRS Data – January 14-17, 2004

**Retrieval QI Flags (Vers. 4.0)**

- Temperature and moisture profiles
-   ≈ 50km spacing
-   1 K in 1 km layer for T
-   20% in 2 km layers for RH
-   profiles assigned quality values
-     by science team
- Quality indicators
-   identify retrieval process
-   layer quality checks
-   only temperature for V4.0

**IR image 15 January 2004 0030 UTC**

**Distribution of AIRS Data by QI**

- 2141 UTC

- 2329 UTC

**Full retrieval     Sfc flagged Sfc+Bot flagged**

**Sfc+Bot+Mid flagged   All levels flagged     No retrieval**

## Collaborative work with Keith Brewster (CAPS, OU) has shown that AIRS data should be spread vertically over 1500 m layers in analysis systems
Results for January 2004 compares favorably
QIs have congruent trends but different magnitudes

- Sfc + Bot + Mid flagged

- All retrievals

- Full

- Sfc + Bot flagged

- All Levels flagged

- Sfc flagged

- 700 hPa

- Collaborative work with Keith Brewster (CAPS, OU) has shown that AIRS data should be spread vertically over 1500 m layers in analysis systems
- Results for January 2004 compares favorably
- QIs have congruent trends but different magnitudes

## Application to Analysis Systems:  Error Tables

-   RMS errors improve as data quality improves
-   Errors slightly larger than AIRS Science Team values (0.9-1.3K, 20% globally)
-   Temperature trend as expected; moisture influenced by marine inversion
-   Generate analysis error tables using these results

- Sfc + Bot + Mid flagged

- All retrievals

- Full

- Sfc + Bot flagged

- All Levels flagged

- Sfc flagged

**1500 m layer average**

- How do we use this information in ADAS?

## AIRS Data Assimilation in WRF

_**Establish assimilation methodology and demonstrate short term weather forecast improvement with AIRS profiles**_
**FY04: **_**Initial case studies**_** over SEUS  (reported on last year)**
** ****LAPS/MM5 (previous experience with surface fields)**
** ****AIRS Vers.3.6 un-validated soundings (mainly over land)**
** ****Limited quality flags**
***      ******Previous work: ***_***Limited impact (mainly upper level temperature)***_
**FY05:**_** Adapt methodologies for ADAS / WRF  - **_**west coast US winter-time storm system (14-16 January 2004)**
** ****ADAS (flexibility, tunable for unique datasets)**
** ****WRF- operational use by NCEP**
** ****AIRS Vers.4.0 validated soundings – T & q ocean profiles only**
** ****T - **_**quality flags important for proper data assimilation**_

**Notes:**
A research focus of SPoRT involves the assimilation of AIRS profiles into regional forecast models with the goal of improving 0-24h regional weather forecasts.  Previous work had very limited positive impact because of the maturing of assimilation procedures and retrieval products.  Current efforts focus on using ARPS Data Analysis System (ADAS) and the latest AIRS profiles of temperature and moisture for an ocean case study.  AIRS quality indicators are used to partition the AIRS retrievals for appropriate assimilation.

## January 14-17, 2004 Case Study

**Slow moving synoptic system off west coast – inadequate forecasts with conventional models**

- Infrared image on 15 January 2004

**2141 UTC **

**2329 UTC **

_**Case selection**_
** ****weather system over ocean**
** ****varied cloud cover**
** ****coverage from AIRS – multiple**
**     ****assimilation times**
** ****availability of AIRS version 4.0 **
**     ****profiles**
** ****applicable to SPoRT SEUS **
**     ****situations (data void over Gulf)**

## ADAS Bratseth Method

**ADAS Bratseth Method**

- Used iteratively to update a first-guess (or background) field provided by a model forecast.  The correction,  ** , at each grid point is given by
- where ***x**(k+1*) is the analysis for the *k**th* iteration,
***x**(k*) is the analysis value at the grid point (background value if *k =1*),
*[****i**obs** - ****i**(k*)] is the value of the innovations (obs. - bckgrd), and
- xi is the weighting function.
- The xi is a function of
- 	_observation and background error variances (error tables)_,
- 	_distance of the observations_ from the grid point
- and is proportional to
- where rij and Δzij - horizontal / vertical distances between obs. and grid
- R and Rz - _horizontal and vertical scaling factors_.

## ADAS Horizontal and Vertical Resolution Factors

**Resolution factors can control influence of AIRS data on resulting assimilated field**
** ****select factors consistent with AIRS**
**      ****vertical and horizontal resolution**
** ****relative magnitude w.r.t other**
**     ****assimilated data is important**

- ADAS Resolution Factors used with AIRS Profiles

## Influence of Data Type in ADAS

**While error variances useful to quantify data errors, “representativeness” of the data type is important to establish **_**relative weights**_** of each data input**
** ****Vertical resolution and accuracy of AIRS – varies between T, q**
** ****Interplays with vertical/horizontal influence factors**

- AIRS values taken from V4.0 validation results

- Data source weights used in ADAS – no raob

- If raobs are also assimilated, relative weights of AIRS data should be reduced (i.e., AIRS error table values should be increased)

- Background

- AIRS

- Background

- AIRS

## ADAS and AIRS Data Assimilation

**AIRS assimilated 850mb T at 2200UTC on 14 January 2004 - 4h WRF as background**

_**ADAS example**_**:**
** ****WRF 4h forecast (upper left) used as background for 2200UTC assimilation**
** ****analysis of AIRS data (lower left) with selected QIs**
** ****AIRS and MADIS data assimilated to produce new analysis field (upper right)**
** ****impact of assimilation (mainly AIRS data seen in difference field (lower right) – 0.5-1.0C changes**
**AIRS changes the analysis – does it change the forecast?**
**  **

## SPoRT Research WRF for AIRS Assimilation

**30km domain with 37 vertical levels**
**Initialized with NCEP 1° GFS grids, with 6-h forecasts used as LBC **
**Numerical experiments:**
**Control (CNTL):    Conventional meteorological data (NO AIRS)**
**AIRS Best Quality (FULL):    Conventional AND full-retrieval AIRS**
**Surface & Bottom Fail (SFBT):   Surface- and bottom-failed AIRS **

## Assimilation Cycles

## L

**L**

**CNTL 500 hPa Temperature**

## Impact of AIRS Data on Moisture Forecast 24-hour Forecast Valid at 00 UTC January 16 2004:  700 hPa

**L**

**CNTL 700 hPa Mixing Ratio**

## Verification Statistics

_**CNTL**_
**   ****Temp RMSE increases with height **
**   ****Moisture RMSE decreases with height**

_**FULL (16%)**_
**   ****Slight T improvement 600 to 250 hPa**
**   ****Degrades q at 700 hPa and below**

_**SFBT (60%)**_
**  ****More significant T improvement between**
**      ****600 and 250 hPa**
**  ****Improved moisture 700 to 500 hPa but**
**     ****degradation below 700 hPa**

**Verification Statistics**
**24h fcst valid 00 UTC 16 Jan 2004**

## NEWT Error Tables

**  ****Reduce impact of lower quality AIRS by increasing error values**

**  ****Retain impact of higher quality AIRS above failed quality layer**

**NEWT Error Tables**

## Verification Statistics

_**CNTL**_
**   ****Temp RMSE increases with height **
**   ****Moisture RMSE decreases with height**

_**FULL (16%)**_
**   ****Slight T improvement 600 to 250 hPa**
**   ****Degrades q at 700 hPa and below**

_**SFBT (60%)**_
**  ****More significant T improvement between**
**      ****600 and 250 hPa**
**  ****Improved moisture 700 to 500 hPa but**
**     ****degradation below 700 hPa**

_**NEWT**_
**   ****Improves moisture forecast near the**
**      ****surface**
**   ****The suppression of poor-quality low-**
**       ****level information are not fully achieved**
**        – ****need a new error table**

**Verification Statistics**
**24h fcst valid 00 UTC 16 Jan 2004**

## ADAS/WRF Summary

_**Assimilation of AIRS profiles can have a positive impact on short-term weather forecasts**_
** ****Make prudent use of AIRS profiles based on quality indicators (QIs)**
** ****Adapt ADAS parameters to represent AIRS vertical / horizontal resolution**
** ****Construct error profiles tailored for AIRS and relative to other data sets**
_**Working closely with AIRS science team (Susskind) on QIs **_
*** ******quality at each level***
*** ******separate indicators for T and q***
_**Need for more case studies**_
_**Evaluate use of AIRS vers. 5.0 retrievals**_
_**Smart data thinning based on QIs, meteorological gradients,etc.**_