1. HARGLO-2 Wind Intercomparisons
Geary Schwemmer, Bruce Gentry
NASA GSFC
Tom Wilkerson
USU
Wind Lidar Working Group
Oxnard 2003

2. HARGLO-2 data sets HARLIE-A (automatic) winds HARLIE-M (manual) winds
GLOW winds
SKYCAM winds
Loran-tracked Rawinsonde winds
GPS-tracked Rawinsonde winds
Add number of 30 minute profiles. Discuss number of coincidences. We had one coincidence of a GPS & Loran sonde.
We acquired data from 4 types of instruments in HARGLO-2:
HARLIE – a conical scanning aerosol backscatter lidar,
GLOW – a scanning UV Direct Detection Doppler lidar,
SKYCAM – a visible wide-angle sky video camera for cloud-tracked winds
Rawinsondes – 2 types of sonde wind profiles: Loran tracked (the standard NWS sondes launched twice a day at 00 and 1200 UT) and GPS (which we had launched twice a day at 1500 and 2000 UT). Thus we had 4 sondes launched during each 12 hour daylight period, during which most of the measurements were conducted.
Wind Lidar Working Group
Oxnard 2003

3. Level 2 Data Parameters Horizontal Wind Vectors
30 minute temporal averaging
200 meter vertical averaging
Filter out Error Bars >5 m/s or 15 deg
Wind Lidar Working Group
Oxnard 2003

4. HARGLO-2 Final Intercomparison Matrix
Speed (m/s)
Direction (°)
Bias/(2/n)
HARGLO-2 Final Intercomparison Matrix
(# profiles)
Sonde
(16)
HARLIE-A
(75)
HARLIE-M
(181)
GLOW
(51)
SkyCam
(15)
.43/1.1
-.08/5.8
-1.7/4.4 (45)
-2.7/2.8 (13)
5.1/4.2 (72)
0.3/3.0 (101)
-.10/.35
>5km
<5 km
-18/22 (45)
18/30 (13)
2.6/6.1
2.6/3.3
-35/35 (72)
-14/42 (101)
-15/24
.1/2.6
-1.2/2.6
-12/14
-25/27
-3.8/23
-29/73
Speed Bias: row-column
This is the table that we are filling in as we complete our analysis. Currently, this chart compares all available data for the instrument pairs that have been filled in, rejecting noisy data, that with error bars > 5 m/s or 15 degrees. Currently, because of the potential for systematic bias for linear features in cloud-tracked winds, we are dividing the HARLIE and SKYCAM data into 2 categories: One for data containing contrails and one for data without contrails. This more or less divides the data by altitude, since practically all of the cirrus activity we saw appeared to be contrails, or contrail-like in nature. We are computing the mean and RMS differences between pairs of data sets, with the wind speeds in green above the diagonal in the table, wind directions in yellow and below the diagonal. Within each box is the mean difference on top and and the RMS differences on the bottom. The sign of the mean corresponds to measurements from the instrument in the top row minus the measurement from the instrument in the left hand column.
We will be comparing data on a point by point data, for points coincident in time (<30 minutes) and altitude.
Wind Lidar Working Group
Oxnard 2003
Direction Bias: column-row

5. Two sondes 19-20 Nov 2001 2300-0040UT + Loran X GPS
Wind Lidar Working Group
Oxnard 2003

6. Status of Analysis All data sets QC’d and reprocessed:
HARLIE & SKYCAM contrail data identified for separate analysis
Sonde data regridded using 200 m averaging of high resolution data
GLOW data reprocessed, regridding signals on 200 m centers, using sonde profiles for temperature corrections
Revised statistical analysis underway, to be completed within 2 months
Wind Lidar Working Group
Oxnard 2003

7. Closing Will sonde measurements be very useful for Cal-Val?
Is a scanning ground-based lidar wind representative for the scan area?
Are the spatial-temporal parameters the right size for Cal-Val?
An airborne system is needed to extend the local measurement made with ground-based profilers to scales commensurate with space-borne wind pixel scales (~50-200km) with dense sampling.
Due to their instant point measurements and the high variability of the atmosphere on scales down to 10’s of meters, sonde wind measurements will not always be very representative of the bulk wind properties over spatial scales of >10’s of km horizontal and 100’s of meters in the vertical. Certainly, we see that because of their high vertical sampling, sondes can be useful for sampling and averaging in the vertical. The scanning lidars use information over km in the vertical to derive a wind vector. Is it representative?
Wind Lidar Working Group
Oxnard 2003