# Presentation: 028978

## Guidance for Targeted Observations during N-AMMA and data impact results: 3 studies

- 1. Sharanya Majumdar  (RSMAS/U.Miami)
- 2. Jason Dunion & Sim Aberson (NOAA/AOML/HRD)
- 3. Rolf Langland (NRL Monterey)
- Acknowledgments: NASA N-AMMA, NOAA THORPEX, NOAA JHT, ONR
- TCSP/N-AMMA Workshop, Baltimore, May 15-17 2007

## 1. Targeted Wind Observations

- In order to improve a 2-day forecast of 700 hPa winds offshore of west Africa, in which locations should we collect supplementary observations?

- ti

- to

- tv

- Ensemble
- Initialization time

- Observing time

- Verification time

- t

- 2-3 days

- 2 days

## Targeted observing method: Ensemble Transform Kalman Filter (ETKF)

- Theory: Bishop et al. (MWR 2001)
- Application to winter storm reconnaissance: Majumdar et al. (MWR 2002, QJRMS 2002)
- Comparison with other methods for tropical cyclones: Majumdar et al. (MWR 2006)
- Need to account for:
- Observation variables, operators, error (co)variance
- Data assimilation scheme
- Forward propagation of error variance between observation and verification times

## Daily Guidance

- Predictions made 48 hours prior to observing time.
- 145-member NCEP, ECMWF and CMC ensembles
- Guidance was available daily by 1400 UTC on
- _[http://](http://orca.rsmas.miami.edu/~majumdar/amma/)__[orca.rsmas.miami.edu/~majumdar/amma](http://orca.rsmas.miami.edu/~majumdar/amma/)__[/](http://orca.rsmas.miami.edu/~majumdar/amma/)_
- EXAMPLE OF GUIDANCE: AEW that developed into Helene, 9/11/06-9/15/06

## A

## A

## Slide 7

## Future Work

- Qualitative understanding of ETKF guidance.
- Identify average ‘sensitive regions’ over season.
- Evaluate ability of ETKF to predict (in)sensitive areas for assimilation of observations.
- Larger ensemble => better ETKF (use THORPEX TIGGE?)
- Sensitivity to variables?  (winds, T, mixing ratio?)
- Data-impact experiments.

## 2. Importance of Relative Humidity

- Saharan Air Layer Experiment (SALEX)
- NOAA G-IV Mission 050807n

- Satellite (SSMI/AMSR-E) imagery of TPW may or may not capture dry air over the ocean that affects hurricane intensity.
- Dry air is sometimes concentrated in a narrow mid-low level jet that is not visible in TPW.  Compare 2 SALEX dropwindsondes ...

## Slide 10

## Numerical model may fail to capture local RH structure and dry air intrusion into hurricane. 

**Drop #17**

**Drop #22**

**17**

**22**

## Potential for Targeted Humidity Observations: Helene 2006

- Location of highest ensemble spread (uncertainty) is in area of high RH gradient.
- These locations are also where satellite and in-situ observations disagree with each other and with models.

## LEFT: Influence of relative humidity (RH) data from dropwindsondes on NCEP GFS Track forecast.  AVHM=with RH.  T254=no RH.  BEST=best track.

- LEFT: Influence of relative humidity (RH) data from dropwindsondes on NCEP GFS Track forecast.  AVHM=with RH.  T254=no RH.  BEST=best track.

- Assimilation of RH dissipates Irene!

- ONE DAY LATER:
- RH maintains Irene!

- ABOVE:  Assimilation of RH data in the dropwindsondes acted to dry the lower troposphere to the north-east of Irene.

## Summary

- SALEX data identified a narrow jet of dry air, not found in satellite data.  Jet may modify hurricane intensity.
- Investigate further using N-AMMA data.
- Thermodynamic data altered NWP forecasts of hurricane track in a few cases.
- Interaction between dry air and hurricane may be better predicted by assimilating RH in high-resolution hurricane models (e.g. HWRF), leading to improved forecasts of intensity change.
- Targeted observing strategies identify locations of high RH gradient in which RH observations ought be assimilated.
- Can generalize dry air intrusion beyond SAL: mid-latitude troughs; dry air advected in inflow (e.g. Hurricane Rita)

## 3. AMMA Data Impact on NOGAPS

**What is the impact of observation data in this region ?**
- 1-30 September 2006: observations assimilated at 00UTC in NOGAPS-NAVDAS (3d-Var) – data received in real-time

## Methodology:

**Methodology:**
-  The impact of each observation can be quantified in terms of how much it reduces (or increases) the short range forecast error on the new analysis trajectory vs. the old background trajectory.
-  The forecast error metric is the 24hr forecast error in the global domain, energy-weighted to combine temperature, moisture, and winds from the surface to near tropopause level: forecast error units = J kg-1
-  Tools required: adjoint versions of the global forecast model and the data assimilation procedure
- Refs: Langland and Baker (Tellus 2004), Langland (MWR 2005)

## Results for observations assimilated at 00UTC 1-30 Sept 2006

**RESULTS:  (negative numbers = error reduction = good!)**
**Observation Type****	****Impact (J kg****-1****) ****	****	****# of Obs**
- Radiosondes		-1.9192			  49,353
- Land-surface		-0.1360			    6,089
- Ship-surface		-0.0218			    1,607
- Geo-sat winds		-1.5486			104,874
- Aircraft			-0.5107			  31,246
- Scatterometer winds	-0.0192			       390
- AMSU-A radiances	-0.4740			  68,697
- SSM/I precip. water	-0.2107			  31,372
**Total Obs                      ****	****-4.8402****	****	****	****293,628**
-  in the region **0****°****N-30****°****N, 30****°****W-30****°****E** – using data received in real-time

## Ranking of 8* most-valuable central African radiosonde stations – in terms of 24hr forecast error metric – NOGAPS-NAVDAS

**Ranking of 8* most-valuable central African radiosonde stations – in terms of 24hr forecast error metric – NOGAPS-NAVDAS**
- Station	   Lat	  Lon	Impact (Jkg-1)    #profiles 	Station Name
- 64700	12.13N	15.03E	 -0.4202	24	Ndjamena, Chad
- 61291	12.53N	7.95W	 -0.2783	28	Bamako, Mali
- 65344	  6.35N	2.38E          -0.2063	26	Cotonou, Benin
- 60018	28.32N	16.38W      -0.1984	26	Tenerife, Canaries
- 61024	16.97N	7.98E          -0.1803	28	Agadez, Niger
- 60680	22.80N	5.43E	 -0.1779	28	Tamanrasset, Algeria
- 61052	13.48N	 2.17E         -0.1188	28	Niamey, Niger
- 61641	14.73N	17.5W	 -0.0978	27	Dakar, Senegal
- Results for observations assimilated at **00UTC** 1-30 Sept 2006 in the region **0°N-30°N, 30°W-30°E**  - using data received in real-time
*** **The combined impact of all 20 radiosonde stations in the selected region = -1.9192 J kg-1, and the impact of all in-situ and satellite observation data  = -4.8402 J kg-1

**Notes:**
Slide 2 color map key:

Large beneficial:
64700, 61291 65344 60018 61024, 60680, 61052, 61641

Moderate beneficial:
65125, 08594, 65510, 64910, 65578, 61223, 61687, 65503

Non-beneficial: 
64750, 61442, 65418, 65330