# Presentation: 780644

##   Evaluation of WINDSAT Data and its impact    on Ocean Surface Analysis and Numerical 		     Weather Prediction			   Robert Atlas Atlantic Oceanographic and Meteorological Laboratory	    NOAA Office of Oceanic and Atmospheric Research	J. Ardizzone, E. Brin, J. C. Jusem, J. Terry, D. Bungato			NASA/GSFC/SAIC

## Slide 2

**                 ****SOURCES OF REMOTELY SENSED **
**                   ****OCEAN SURFACE WIND DATA**
**   ****active     measurements**
** ****original: scatterometer on Seasat                     1978**
** ****AMI scatterometer on (ERS-1)/ERS-2                1991-present**
** ****NSCAT on ADEOS-1                                            1996-1997**
** ****SeaWinds on Quikscat                                        1999-present**
** ****SeaWinds on ADEOS-2****                                      **** 2002-2003**
**    ****passive measurements**
**  ****SSM/I                                                                     1987-present**
**  ****TMI on TRMM                                                        current**
**  ****WindSat (Polarimeter)                                         launched 12/15/02**

## GEOPHYSICAL VALIDATION OF WINDSAT DATA

- Two-level approach to validating WINDSAT data:
- * COLOCATION COMPARISONS : colocation statistics comparing 	WINDSAT data to other sources of surface wind information.
- 	- ships and buoys, low-level aircraft, cloud-tracked winds
- 	- SSM/I and QuikSCAT
- 	- analyses from NCEP, ECMWF and GEOS
- * LIMITED DATA IMPACT EXPERIMENTS : following 		methodology used to evaluate  impact of SSM/I, NSCAT and 	QuikSCAT data.
- 	- using VAM,  NCEP and GEOS data assimilation systems
-    	- objective measures of analysis and forecast accuracy
-    	- subjective comparisons of analyses, model fluxes and 		  forecast fields
-    	- case studies examining specific effects of WINDSAT data

## 	KEY WINDSAT VALIDATION CRITERIA

- * Direction and speed differences between WINDSAT and  	other data types.
- * Objective quality control of WINDSAT data.
- 	- Number of reports rejected.
- 	- Patterns of rejected reports.
- * Synoptic plausibility of WINDSAT wind patterns.
- 	- Coherent, dynamically consistent patterns in space 		and time?
- 	- Agreement with other information (eg. visible 			imagery)?
- * Effect of WINDSAT data on analyses and forecasts.
- 	- On average are analyses and forecasts improved?
- 	- Do significant negative effects occur?
- 	- What is the magnitude of the WINDSAT impact 			relative to QuikSCAT and SSM/I?

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## Collocation Methodology

**Maximum Temporal Displacement: 90 min**
**Maximum Spatial Displacement: 50 km**
**Conventional Data**: All conventional ship and buoy wind reports were adjusted to an instrument height of 10 meters assuming neutral stability. A variational analysis method (VAM) was used to quality check the data as follows:
- 	1. gross outliers eliminated using NCEP analysis.
- 	2. variational analysis of data performed using NCEP analysis (as background) with weak constraints.
- 	3) QC performed relative to the new analysis with tighter constraints.
**Windsat Data**: All relevant quality flags were used including the rain flag, warm load anomaly flag and the wind speed check (< 5m/s OR > 20 m/s not used).
**Quikscat Data:**** **JPL Science Data Product. All relevant quality flags were checked including the rain flag and low wind speed check (< 3m/s not used).
- “Perfect” collocations were performed by choosing the alias closest in direction to the collocated conventional data.
- Collocations are binned in 5m/s intervals according to the Windsat retrieved wind speeds.

## RMS Speed Differences versus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | 2.596 | 2.439 | 2.564 | 3.119 | 12/01/03 – 2/29/04 |
| V190 | 2.430 | 2.188 | 2.455 | 3.160 | 12/01/03 – 2/29/04 |
| Quikscat | 2.343 | 2.010 | 2.393 | 3.308 | 12/01/03 – 2/29/04 |

*RMS Speed Differences versus All Conv*

## Speed Bias versus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | 0.569 | 1.150 | 0.100 | -1.407 | 12/01/03 – 2/29/04 |
| V190 | 0.454 | 1.042 | -0.044 | -1.485 | 12/01/03 – 2/29/04 |
| Quikscat | 0.095 | 0.734 | -0.472 | -1.939 | 12/01/03 – 2/29/04 |

*Speed Bias versus All Conv*

## RMS Direction Differences For Best Aliasversus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | 19.329 | 21.846 | 15.046 | 13.211 | 12/01/03 – 2/29/04 |
| V190 | 17.730 | 20.357 | 12.924 | 11.770 | 12/01/03 – 2/29/04 |
| Quikscat | 18.269 | 19.800 | 15.702 | 15.567 | 12/01/03 – 2/29/04 |

*RMS Direction Differences For Best Alias**versus All Conv*

## RMS Direction Differences For Selected Aliasversus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | 29.112  | 33.676 | 20.634 | 16.793 | 12/01/03 – 2/29/04 |
| V190 | 27.274 | 31.332 | 19.800 | 18.524 | 12/01/03 – 2/29/04 |
| Quikscat | 23.916 | 27.195 | 17.323 | 19.682 | 12/01/03 – 2/29/04 |

*RMS Direction Differences For Selected Alias**versus All Conv*

## Percent Correct versus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | 75.662 | 75.500 | 78.482 | 67.327 | 12/01/03 – 2/29/04 |
| V190 | 74.196 | 75.670 | 73.394 | 66.007 | 12/01/03 – 2/29/04 |
| Quikscat | 93.340 | 90.812 | 97.083 | 97.697 | 12/01/03 – 2/29/04 |

*Percent Correct versus All Conv*

## Directional Bias for Best Alias versus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | -0.534 | -0.381 | -0.577 | -1.535 | 12/01/03 – 2/29/04 |
| V190 | -0.426 | -0.228 | -0.448 | -1.421 | 12/01/03 – 2/29/04 |
| Quikscat | 0.398 | 0.767 | -0.007 | -1.074 | 12/01/03 – 2/29/04 |

*Directional Bias for Best Alias versus All Conv*

## Directional Bias for Selected Alias versus All Conv

| Collocation
data | All Bins | 5-10 | 10-15 | 15-20 | Time Period |
| --- | --- | --- | --- | --- | --- |
| V181 | -1.558 | -1.665 | -1.204 | -2.713 | 12/01/03 – 2/29/04 |
| V190 | -1.946 | -2.071 | -1.344 | -3.155 | 12/01/03 – 2/29/04 |
| Quikscat | 0.464 | 0.846 | 0.287 | -1.878 | 12/01/03 – 2/29/04 |

*Directional Bias for Selected Alias **versus All Conv*

## Impact of WindSAT using Variational Analysis 

- In the following slides, the variational analysis method (VAM; Atlas et al.,1996) is used to evaluate the potential impact of WINDSAT  relative to the NCEP operational analysis, which  includes Quikscat and SSM/I surface wind observations.
- The variational analysis generates a gridded surface wind analysis which minimizes an objective function **F** measuring the misfit of the analysis to the background, the data and certain a priori constraints.  The following expression is used:
-      **F=   ********1****S****C****+  ********2****S****B****+  ********3****S****S****+  ********4****S****VEL****+  ********5****S****DIV****+  ********6****S****VOR****+  ********7****S****DYN**
-      where the ******'s** are weights controlling the amount of influence each constraint.
- Analyses are performed at .25, .5, and 1 degree  resolution, with the NCEP operational analysis as the background for the VAM, and only WINDSAT data added.
- The VAM analyses with WINDSAT show significant modifications to fronts, cyclones, anticylones and other meteorological phenomena.

- Example of WINDSAT data in N. Atlantic

- Impact of WINDSAT on Cyclone in N.Atlantic

- 		NCEP analysis 6 h later

## WINDSAT FORECAST IMPACT EXPERIMENTS 	Using GEOS 4 Operational DAS 

- _EXPERIMENT 1:_
- CONTROL: All Conventional Data + TOVS + CTW + SSM/I TPW
- CONTROL + WINDSAT version
- CONTROL + QuikScat (To be generated)
- FORECASTS: 26   5-day forecasts  from each
- _EXPERIMENT 2: _
- CONTROL: All Conventional Data + TOVS + CTW + SSM/I TPW
- CONTROL + WINDSAT version
- CONTROL + QuikScat (To be generated)

## 			SUMMARY

- A detailed geophysical validation of WINDSAT data is being performed.
- This includes: collocations with in situ data, satellite observations, and model analyses; synoptic evaluations by highly skilled meteorological analysts; objective quality control; and impact experiments using both global and regional data assimilation systems.
- All of the measures thus far indicate potential for WINDSAT to improve ocean surface wind analyses and weather prediction, although there are significant limitations relative to scatterometry.