1. Evaluation of Dropsonde Humidity and Temperature Sensors using IHOP and DYCOMS-II data Junhong (June) Wang Hal Cole NCAR/ATD Acknowledgement: Kate Young, Dean Lauritsen, Terry Hock, and Krista Laursen (all ATD), Matthew Coleman (PennState U.) Wang (2004, submitted to JTECH)

2. Motivations 1.Under-utilization of dropsonde humidity data in Hurricane forecasting, 2.Dry biases in dropsonde data suggested by previous studies, 3.Comparisons of dropsonde and LASE data during IHOP, 4.More field projects used dropsonde data to map moisture and validate remote sensors, 5.Our experiences with radiosonde humidity data.

3. Data courtesy Sim Aberson, HRD Thanks to James Franklin, NOAA/AOML/NHC

4. CAMEX-3 CAMEX-4 From Kooi et al. (2002) % MR difference between LASE and dropsonde Humidity dry bias from pervious studies From Vance et al. (2004) RD93-TWC RD93-RS90 ~8%

5. Lear dropsondes were in good agreement overall (<5%), but Falcon dropsondes were consistently drier by ~8%. LASE-Dropsonde Comparisons (<75 km & <75 min) Courtesy Ed Browell, NASA/LARC + DLR-DIAL Comparisons with Dropsondes Courtesy Gehard Ehret (DLR)

6. Errors/Biases in Dropsonde Humidity Data 1. Contamination dry bias due to outgassing from the sensor packaging material, sensor bulk head, the outer tube and others, 2. Humidity time lag error, 3. Sensor wetting or icing.

7. Data from two field experiments 1.IHOP_2002 (SGP, May-June 2002): 71 pairs of co-incident dropsonde and radiosonde soundings for intercomparisons,71 pairs of co-incident dropsonde and radiosonde soundings for intercomparisons, Comparisons of old and young sensors.Comparisons of old and young sensors. 2.DYCOMS-II (NE Pacific, July 2001): All 63 dropsondes into marine stratocumulus clouds,All 63 dropsondes into marine stratocumulus clouds, Comparisons with co-incident airborne ascending and descending data.Comparisons with co-incident airborne ascending and descending data. DYCOMS-II

8. Comparisons with radiosonde data (IHOP) Total 420 dropsondes from two aircrafts and for four types of missions Total 2879 radiosondes from 19 fixed stations and three mobile systems Total 158 pairs within 50 km and half hour, and 71 sampled the same air masses based on visual examination.

9. June 9, 18 UTC RH T Q

10. Mean Differences (Dropsonde-Radiosonde) RH TQ

11. Heat conduction to explain the cold bias 1. Inside 2. outside 3. reach equilibrium 4. in the flight The bulk-head and sensor boom are warmer than the environment, so conduct heat to the sensors: Tm > Ta and RH2 < RH1 Sensors come from colder to warmer air, so sensors lose heat to the BH/SB : Tm < Ta and RH1-RH2 Colder dropsonde T than radiosonde in IHOP (~0.4  C) RH2 RH1 T

13. Ages of PTU sensors for IHOP Sonde built dates: Feb-Apr 2002

14. Comparisons of old and new dropsondes <20 km, < 40 min

15. Performance in Clouds (Dycoms-II) Marine Stratus Cumulus clouds

16. InstrumentVariablesRangePrecisionAccuracy Vaisala Dropsonde RD93: H-HUMICAP thin film capacitor BAROCAP silicon sensor THERMOCAP capacitive bead Codeless GPS receiver GPS 121 RH pressure temperature wind 0-100% 1080-3 hPa -90  C to 60  C 0-200 m/s 1% 0.1 hPa 0.1  C 0.1 m/s 2%* 0.4 hPa* 0.2  C* +0.5 m/s NCAR Lyman-alpha hygrometer (“stub” and cross-flow) mixing ratio0.1-25 g/m 3 0.2%5% GE 1011B Dew Point Hygrometer dew point temperature -65  C to 50  C0.006  C0.5  C (>0  C) 1.0  C (<0  C) Rosemount temperature sensortemperature -60  C to 40  C0.006  C1.0  C PMS Liquid Water Sensorliquid water content 0-5 g/ m 3 0.001 g/m 3 0.02 g/m 3 Specifications of different sensors during DYCOMS-II

17. Matching dropsonde with C-130 ascending/descending profile Descending Ascending Overshooting

18. Time-lag Error Mean estimated time constant of ~5 s is larger than 0.5 s given by the manufacture.

19. Sensor Wetting  Introduce alternative heating of twin humidity sensors to speed up the evaporation

20. Performance of the Temperature Sensor: Wetting Error? Wetting error in airborne in- situ T sensors (e.g. Eastin 2002): ~1-3  C for Rosemount.

21. Summary on Dropsonde Evaluation 1.Dry Bias: 1.Dry Bias: No systematic dry bias is found in dropsonde humidity data as suggested by previous studies. 2.In Clouds: 2.In Clouds: The maximum RH inside clouds does not show 100% all the time, but is within the sensor accuracy range (95-100%). 3.Time Lag Errors: 3.Time Lag Errors: The dropsonde humidity sensor experienced large time-lag errors when it descended from a very dry environment above clouds into clouds. Mean estimated time-constant of the sensor is 5 s at 15  C, which is much larger than 0.5 s at 20  C given by the manufacture. 4.Sensor Wetting: 4.Sensor Wetting: The dropsonde humidity sensor still reported near- saturation RH after it exited clouds because of water on the sensor. The alternative sensor heating for twin humidity sensor (not currently implemented) might help speeding up evaporation of the water. 5.Temperature: Another sensor wetting effect is on temperature data. The DYCOMS-II comparison show colder dropsonde temperatures inside and below clouds by 0.21  C and 0.93  C, respectively. The IHOP data also show ~0.4  C colder dropsonde data, which might be due to the heat conduction between sensors and the bulk head and sensor boom.

22. Comparisons of old and new sondes (Ocean Waves) Comparisons of old and new sondes (Ocean Waves) 12 sensors older than 1.5 years 6/021/02

23. BAMEX

24. Comparisons with co-incident radiosonde data (BAMEX) Comparisons with co-incident radiosonde data (BAMEX)

25. Sensor wetting (“wet-bulb”) Rev D sonde? (BAMEX) Sensor wetting (“wet-bulb”) Rev D sonde? (BAMEX) 22/440