SeaWinds Empirical Rain Correction Using AMSR January 17, 2005 Bryan Stiles, Svetla Hristova-Veleva, and Scott Dunbar

2 Outline Method Overview –Data set description –Variance computation change in objective function –Rain correction methods Performance Summary Metrics –Direction and Speed Histograms of DIRTH vectors –2-D NCEP/Retrieved Relative Direction Histograms –Cross Track Bias (by liquid and speed) –Speed Bias (by liquid and speed) –RMS Direction Difference (by liquid and speed) Discussion

3 Method Overview The entire SeaWinds mission was processed 3 ways. –Climatological attenuation correction only (SCAT) –Physically based rain correction (PHY) –Empirically based rain correction (EMP) The objective function was modified for all three cases. –Log(var) term was put in. –Variance was modified so that: assuming the standard deviation of the backscatter correction b was 50% noting that measurement noise was multiplied by the attenuation correction a.

4 Method Overview PHY –Uses physical models of attenuation and backscatter to compute a and b from AMSR liquid, vapor, SST, and rain rate. –s is the splash ratio as a function of rain rate EMP –Estimates a and b as function of liquid, vapor, and SST using NCEP winds collocated with SeaWinds  0 values. –To avoid biases due to NCEP errors Scaled a to match physical liquid=0 values. Scaled b so that minimum backscatter was 0.

5 Performance summary Rain free cases are not affected significantly by the corrections. Both rain corrections improve speed bias and reduce cross track direction preference. RMS direction performance is mixed –Nearest RMS direction difference from NCEP is increased by the correction techniques, but that may be explained by: The number of ambiguities decreases in the corrected cases for liquids over 1 mm. –Selected RMS direction difference has little change One would expect improvement due to reduced cross track preference. Lack of improvement may indicate an additional directional noise imparted by the corrections. –DIRTH RMS direction difference is significantly decreased especially for the empirical correction. DIRTH tends to smooth out directional noise in the corrected winds.

6 Performance summary (cont) RMS speed performance –RMS speed differences (not shown) decrease due to speed bias improvement –Speed variance increases; especially for the empirical case.

7 1-D Direction and Speed Histograms Plot format –NCEP Histograms were plotted together with the DIRTH vector histograms for each correction method. –Direction and Speed Histograms were computed for varying: Correction Strategy (line color) Geographic region (plot in slide) Liquid Range (slide) –Percentage of Data in each liquid range is noted. Observations –Corrections tend to match model direction histograms better –Corrections tend to follow model wind speed trends by geographic region –DIRTH creates cardinal direction spikes (investigating …)

8 Rain Free, 86% of data

mm, 8.6% of data

mm, 3.3% of data

mm, 1.0% of data

mm, 0.4% of data

mm, 0.03% of data

14 2-D Direction Histograms Two dimensional histograms of retrieved direction and NCEP direction, relative to the s/c flight direction. Demonstrates the removal of rain-related artifacts [e.g. cross-track directions]. Histograms were computed for varying –Correction method (slide) –Liquid range (plot in slide) –Choice of DIRTH, Selected, or Nearest (slide) SCAT-only histograms repeated as the top row of each slide for comparison. –SCAT-only histograms differ for EMP and PHY slightly due to differences in flagging.

15 EMP, Nearest

16 PHY, NEAREST

17 EMP, Selected

18 PHY, Selected

19 EMP, DIRTH

20 PHY, DIRTH

21 Plot Formats Metrics from here on are: –Plotted as a function of Liquid (x-axis) (full range or 0-3 mm) –Due to a bug liquids values on the x-axis are 4 times the true values. NCEP speed (multiple plots in slide) Correction method (line color, cyan=EMP, red=PHY, black=SCAT, dotted black=SCAT w/o log(var)) –Computed for 200 orbits of SeaWinds data. –Plots for full liquid range and 0-3 mm (99.97% of data) are on separate slides.

22 Speed Biases Metric Definition –Selected speed - NCEP speed Performance Summary –Nearest and DIRTH speed biases (not shown) are similar. –Significant improvement for all but highest wind speeds. –Even heavy rain cases show improvement. –Correction imparts little or no change for rain free data. –Slight change in rain free biases with addition of log(var).

23 Speed Bias, All Liquids (Liquid x-axis values are 4X true liquid values)

24 Speed Bias, 1-3 mm (Liquid x-axis values are 4X true liquid values)

25 Cross Track Direction Bias Metric Definition –The average amount closer to the cross swath than NCEP in degrees. Angle between NCEP and cross swath minus the angle between selected and cross swath. A positive value indicates the cross track direction is preferentially retrieved. Performance Summary –Corrections reduce rain induced preference for cross swath direction. –Nearest and DIRTH performance is similar to Selected. –Full liquid range and 0-3 mm plots are shown.

26 Cross Track Bias, All Liquids (Liquid x-axis values are 4X true liquid values)

27 Cross Track Bias, 1-3 mm (Liquid x-axis values are 4X true liquid values)

28 RMS Direction Difference Nearest, Selected, and DIRTH stats are plotted. Performance Summary –Correction increases Nearest RMS direction difference in rain. Number of ambiguities are reduced. Correction noise is added. –Selected direction difference - no change Correction noise competes with removal of cross track preference. –DIRTH direction difference - improvement with rain correction Best case DIRTH spatially smooths correction noise. Worst case DIRTH smooths directional features in rain.

29 RMS Direction Diff, Nearest (Liquid x-axis values are 4X true liquid values)

30 Number of Ambiguities (Liquid x-axis values are 4X true liquid values)

31 RMS, Direction Diff, Selected (Liquid x-axis values are 4X true liquid values)

32 RMS Direction Diff, DIRTH (Liquid x-axis values are 4X true liquid values)

33 RMS Dir. Diff, DIRTH, All Liquids (Liquid x-axis values are 4X true liquid values)

34 Discussion What further validation is needed? What can change analysis tell us? Should change analysis look at: –DIRTH solution performance? –Cross Track Direction Bias? What can we compare with besides NCEP? Buoys?