Evaluation of Dropsonde Humidity and Temperature Sensors using IHOP and DYCOMS-II data

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

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

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

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

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

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

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. T Q RH Mean Differences (Dropsonde-Radiosonde)

11. 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) Heat conduction to explain the cold bias The bulk-head and sensor boom are warmer than the environment, so conduct heat to the sensors: Tm > Ta and RH2 < RH1 1. Inside 2. outside RH2 3. reach equilibrium RH1 T 4. in the flight

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

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

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

16. Specifications of different sensors during DYCOMS-II

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

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

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

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 • Dry Bias: No systematic dry bias is found in dropsonde humidity data as suggested by previous studies. • In Clouds: The maximum RH inside clouds does not show 100% all the time, but is within the sensor accuracy range (95-100%). • 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. • 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. • 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.