# Presentation: 883092

## New quality indicators for GOES-derived winds and their effect on NWP data impact experiments

-    NOAA THORPEX PI WORKSHOP
-   January 17-19   Camp Springs, MD

- Howard Berger1, Chris Velden1 and John Le Marshall2
- 1Cooperative Institute for Meteorological Satellite
- Studies - University of Wisconsin
- 2Joint Center for Satellite Data Assimilation

## Outline

- Atmospheric Motion Vector (AMV) Introduction
- Quality Indicators
- Expected Error
- Spatially Correlated Error
- Potential Data Impact Experiments
- Conclusions

## What are AMVs?

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- Infrared Imagery

- Vectors derived from satellite imagery by tracking tracers (clouds or areas of water vapor) through successive images.

**What are AMVs?**

## What are AMVs?

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- T

- T + 30 min

- Infrared Imagery

- Vectors derived from satellite imagery by tracking tracers (clouds or areas of water vapor) through successive images.

**What are AMVs?**

## What are AMVs?

**What are AMVs?**

- T - 30 min

- T

- T + 30 min

- Infrared Imagery

- Vectors derived from satellite imagery by tracking tracers (clouds or areas of water vapor) through successive images.

## Also Crucial:

- _Height Assignment:_
- Largest Source of Error
- _Quality Control:_
- 	Data adjustment and quality flags

## Automated Quality Control

- Run at satellite data processing centers. Produce quality index:
- NESDIS: 	RFF,QI
- EUMETSAT:   	QI
- JMA: 		RFF,QI
- QI	= Quality Indicator
- RFF = Recursive Filter Flag

## QI:

- AMVs undergo various vector consistency checks and are given a normalized score between 0 and 1.

- RFF:
- AMVs are subject to two passes of a 3-D recursive filter objective analysis.
- The filter determines fit of each vector to an analysis field. (Between 0 and 100).
- Higher scores are “better” winds.

## Le Marshall et al., 2004. Error characterization of atmospheric motion vectors. Aust. Met. Mag., 53, 123-31.

- Expected Error

- Quality Indicator developed and tested using GOES datasets (in collaboration with John LeMarshall of JCSDA).
- The “Expected Error” (EE) shows an improved relationship with measured error (vs. raobs) than current AMV quality indicators
- EE in terms of vector error rather than normalized quantity
- Lower values are better

## Expected Error

- QI Speed Test
- QI Direction Test
- QI Vector Difference Test
- QI Consistency Test
- QI Forecast Test
- Wind Speed
- Assigned Pressure Level
- Wind Shear (200 hPa below, 200 hPa above)
- Temperature Gradient (200 hPa below, 200 hPa above)

- Multiple Linear Regression  of AMV - raob
- Differences based on:

## Expected Error at CIMSS:

- Building statistics for the following GOES AMV sets:
- N.H InfraRed (NHIR)
- N.H. Water-Vapor (NHWV)
- N.H. Visible (NHVS)
- N.H. ShortWave InfraRed (SWIR)
- S.H. InfraRed(SHIR)
- S.H. WaterVapor (SHWV)

##      Regression coefficients are calculated from a minimum of two months of data to a maximum of three months of data.

- NHIR 11/01/2005 - 01/12/06

| Parameter | Coefficient | Norm. Coefficient |
| --- | --- | --- |
| Const | 16.19 | 0 |
|  QI - Speed  |  -3.54 | -0.079 |
| QI Direction | -2.45 | -0.048 |
| QI Vector Difference | 2.83 | 0.089 |
| QI Consistency | -1.17 | -0.025 |
|  QI Forecast  | -3.25 | -0.092 |
| Speed | 5.7x10-2 | -0.091 |
| Pressure | -7.3x10-3 | -0.21 |
| SHEAR  | 0.09 | 0.081 |
| Vertical Temp Gradient | 0.05 | 0.12 |

##   EE and QI versus (AMV - raob) difference

-          Northern Hemisphere Infrared

- QI

## NHIR

- NHWV

- NHVS

- SWIR

## Expected Error Summary

- Expected Error regresses QI components and additional information to produce a skillful measure of vector error.
- Expressing error in terms of m/s is more physically meaningful than a normalized score
- Expected Error shows more skill than QI at evaluating “quality” of AMVs versus radiosondes.

## AMVs from multiple satellites are assimilated at all global NWP centers with generally positive impact. 

- Velden *et al.*,	2005 BAMS
- Kelly 	2004 (ECMWF)
- Xiao Q. *et al.*, 	2002 Mon Wea Rev.(NORPEX)
- Soden B. *et al.,* 	2001 Mon Wea. Rev (GFDL)
- Goerss, J. *et al.,* 	1998 Mon Wea. Rev (NOGAPS)
- Langland R. *et al.,* 	1999 BAMS (NORPEX)

- Berger, H, M. Forsythe		2004 (UK Met Office) NWP Tech. Report 451
- Su, X. *et al.*, 		2003 NCEP Tech. Note 440

- However, there are also mixed results, especially with variational assimlation schemes:

## One possible explanation for the mixed impact:

- Bormann et al. (2004) compared AMV data to
- co-located radiosondes showing statistically significant spatial error correlations.
- With routines in EE software we can estimate correlated error and disseminate that information to NWP centers.

## Spatially correlated error

- 100 km bins
- NHIR Upper level AMVs

- Sept 2005 - Jan 2006

## Advantages of Rapid Scan AMVs

- Tracked targets are more stable between frequent images (e.g. low-level cumulus clouds and high-level cirrus).
- More targets at higher resolution pass automated quality control
- Better able to capture mesoscale features
- “Optimal” time interval is dependant on channel

- Velden *et al.*, BAMS 2005

## TROPEX

- (TROpical cyclone Predictability EXperiments)
- http://cimss.ssec.wisc.edu/tropic/tropex/

- Collaboration between CIMSS, the Office of Naval Research (ONR), and Naval Research Laboratory
- Special Rapid-Scan AMV sets (5-minute image interval) during the Atlantic Hurricane Season in 2006
- AMV sets used in NOGAPS models for data impact experiments, targeting experiments and impacts on Tropical Cyclone track and intensity forecasting.

## Upper-Level GOES IR AMVs 

- Valid 00Z August 29th, 2005

## TROPEX Expected Error relationship

- 		NH InfraRed

## Upper-Level GOES IR Winds 

-                             Expected Error: 0.00 - 3.0 ms-1  3.01 - 4.5 ms-1
-                                                                   4.51 - 6.0 ms-1  6.01 - 9.0 ms-1

- Upper-Level GOES IR Winds
- Valid 00Z August 29th, 2005

## TROPEX Error Correlations 

- TROPEX Error Correlations
- NHIR - High Level

## Future Data Impact Experiments

- NCEP Impact Experiment
- Investigate impact of RS AMVs on GFS model.
-  Use expected error as QC for Data Impact Experiments
- TROPEX NOGAPS Experiment
- Investigating targeted sampling and AMV Rapid-Scan impacts on Tropical Cyclone track and intensity forecasting

## Summary

- Expected error (EE) is a promising quality control parameter calculated operationally at CIMSS and being operationally implemented at NESDIS.
- The EE has skill in estimating the error of an AMV (with respect to raobs). It can be a useful QC check for AMV production and, we hope to show, for data impact experiments.
- GOES Rapid-Scan AMVs produced for TROPEX have potential for improving NWP, particularly for tropical cyclone track and intensity forecasting.
- Future experiments with the EE and correlated error (CE) will examine how AMVs can be better utilized within NWP.