# Presentation: 138329

## The Use of AIRS Profiles in Short-term Weather Forecasts: A Case for Enhanced Quality Indicators

**Gary Jedlovec**
**NASA / Marshall Space Flight Center**
**Bill Lapenta – NASA/MSFC (detailed to HQs)**
**Brad Zavodsky - Univ. of Alabama**
**Shih-hung Chou – NASA/MSFC**
**AIRS Science Team - September 2005**

**Notes:**
Presentation made to the AIRS Science Team on September 29, 2005 in Greentbelt MD.

## NASA’s Short-term Prediction and Research Transition (SPoRT) Center

_***Mission:***_*** Apply NASA measurement systems and unique Earth science research to improve the accuracy of short-term (0-24 hr) weather prediction at ***_***the regional and local scale***_***      (http://weather.msfc.nasa.gov/sport/)***

_**Transition **_**research capabilities to operations**
** ****real-time MODIS data and products to 6 NWS forecast offices**
** ****twice daily WRF model output (initialized with MODIS SSTs)- **_**operational**_
** ****convective initiation / lightning products for nowcasting severe weather**
_**Development of new products **_**and capabilities for transition**
** ****MODIS SST composites**
** ****radiance data assimilation w/ filtered radiances (NASA Fellowship student)**
** ****assimilation of AIRS profiles into SPoRT WRF**

**Notes:**
The SPoRT program transitions research capabilities to the operational community, specifically regional NWS forecast offices.  Data currently transitioned includes MODIS imagery and selected products, AMSR-E products, convective initiation parameters , lighting data, and WRF forecasts in real-time to address specific forecast problems. Ongoing research activities included radiance and profile assimilation techniques for regional forecast model improvements. 

## How We Operate

**NASA/MSFC Earth and Planetary Sciences Branch collocated with UAH and the Huntsville NWS Forecast Office at the NSSTC –  ****regular interactions facilitate a test-bed environment**

**SMD funded with supporting Applications program initiatives**

_***Problem driven***_** rapid proto-typing and transitional activity**
** ****provide real-time data and products to meet NWS forecaster needs**
** ****operational WRF output with MODIS SSTs**
** ****training – product modules, science sharing with NASA / UAH **

**Notes:**
The SPoRT Center is part of Earth and Planetary Science Branch at NASA’s MSFC and located in the National Space Science and Technology Center (NSSTC) building along with the University of Alabama in Huntsville’s Atmospheric Science Department and the Huntsville NWS Forecast Office.   The collocation allows for regular interactions and effective transitional activities to the operational environment.

The SPoRT program is funded out of NASA’s Science Mission Directorate at NASA HQs with supporting Application Program initiatives.

The SPoRT activity is forecast problem driven – NASA capabilities are matched with NWS forecast problems through direct interaction with individual forecasters. Data and capabilities transitioned to the NWS WFOs address specific office needs.  SPoRT also provides various types of training to the fields forecasters on these new products.

## AIRS Data Assimilation in WRF

_**Establish assimilation methodology and demonstrate short term weather forecast improvement with AIRS profiles**_
**Initial case studies over SEUS – relevant to SPoRT WFOs**
** ****LAPS (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)***_
_**Recent initiative**_** – west coast US winter-time storm system (14-16 January 2004)**
** ****ADAS (flexibility, tunable for unique datasets)**
** ****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 14-16, 2004 Case Study

**Slow moving synoptic system off west coast – in-adequate forecasts with conventional models**

- Infrared image on 14 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)**

## SPoRT Research WRF for AIRS Assimilation

**SPoRT **_**Research**_** WRF for AIRS Assimilation**

**ADAS**

**ADAS**

**18 UTC**

**00 UTC**

**00 UTC**

**2h WRF Forecast**

**22 UTC**

**30km domain with 37 vertical levels**
**Dynamics and Physics**
**Eulerian mass core**
**Dudhia SW radiation**
**RRTM LW radiation**
**YSU PBL, Noah LSM**
**Ferrier microphysics**
**Kain-Fritsch**
**Initialized with NCEP 1****°**** GFS grids, with 6-h forecasts used as LBC **
_**Assimilation / forecast**_**   **

**2329UTC**

**2141UTC**

- WRF Forecast Domain

- Validation region

- Validation every 12 h

**48h WRF Forecast initialized from ADAS analysis at 00 UTC**

## WRF Forecasts with AIRS Profiles

**Initial case studies indicate positive impact of AIRS T / q at most levels for 12-48h forecasts**
**Full and surface flag retrievals**
** ****temperature -**
** ****0.2-1.0K improvement in bias - most levels**
** ****0.5K reduction in RMS**
** ****moisture - **
** ****improvement varied**
** ****uncertain performance in lowest levels**
** **
**Performance varies with quality of AIRS profiles used in ADAS**

**AIRS improves WRF short-term forecasts of temperature and moisture**

**Temperature improved at most levels**

**Temperature improved at most levels**

- Based on full and sfc
- flagged retrievals

- 24h forecast

## AIRS Data – January 14-16, 2004

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

**Temperature and moisture profiles**
**  ****~ 50km spacing**
**  ****profiles assigned quality values**
**           ****by science team**
**  ****V4.0 temperature quality flags**
**Quality indicators **
** ****identify retrieval process**
** ****layer quality checks **
**Full and surface flag retrievals**

## AIRS Data Quality Indicators

_**Quality indicators**_** **
** ****identify retrieval process**
** ****layer quality checks**
**Variations in retrieval “quality” based on QI flags can be at times subtle, other times more significant**
_**Reduced quality**_** of profiles seems to be related to the **_**presence of overcast conditions**_
***Separate moisture quality indicators are needed***

**Retrieval variations based on QI**

## 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 and AIRS Data Example Assimilation

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

_**An ADAS example**_**:**
**AIRS data assimilated with 4h WRF forecast as background**
** ****AIRS in first two iterations with**
**   ****coarse vertical and horizontal **
**   ****influence factors**
** ****other data (mainly ACARS, sfc, **
**   ****and few special raobs)**
**   ****assimilated in other iterations**
** ****AIRS error tables with realistic **
**   ****vertical variations and more **
**   ****influence than background**
**  **

## 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 AIRS varies ***
***with ADAS constraints***

**ADAS converges towards AIRS data**

**Vertical Resolution Factor Changes**

## Influence of Data Type in ADAS

**While error variances are 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

## Correlation of AIRS Quality with Model Impact

**Inclusion of AIRS retrievals with varying quality  (additional QI flags)  **_**negatively**_** affects performance over control run at specific levels**
** ****degraded performance  at 925 and 850mb for both temperature and moisture**
**      ****for the 24h forecast (when additional AIRS soundings are used)**
** ****improved performance in middle and upper levels with additional (lower **
**quality) profiles**
**Can we adjust assimilation to minimize negative - maximize positive impact?**

**OPTIMAL –full retrievals**

## Vary AIRS Error Tables with Quality Indicators

- Temperature

**Vary AIRS Error Tables with Quality Indicators**

**Can we adjust assimilation to minimize negative - maximize positive impact?  YES!  **
** ****need to assign AIRS profiles with different QI flags with different    **
**      ****(more appropriate) error table values**
** ****separate quality indicators for temperature and moisture**

- Example error profile for ADAS for AIRS data flagging low-level temperature check

## Summary

**Preliminary results show that the assimilation of **_**AIRS profiles**_** have a **_**positive impact on 0-48h forecasts from the SPoRT WRF**_
_**Performance is dependent**_** on:**
**Configuration of data assimilation scheme (ADAS)**
** ****vertical and horizontal smoothing**
** ****relative weights of AIRS versus other data sources (and background)**
**Use of AIRS quality indicators **
**  ****vary weights in assimilation system based on variation in **
**     ****AIRS quality**
** ****maximize use of all AIRS retrievals**
***Need more quality indicators, especially for moisture***
_***Future work***_***:***
*** ******refine use of profiles in ADAS based on AIRS quality indicators (v5.0?)***
*** ******forecast improvement – basic parameters and skill scores***
*** ******additional case studies are being selected – Gulf coast***