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)
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.
Thanks to
James Franklin,
NOAA/AOML/NHC
Data courtesy
Sim Aberson, HRD
Humidity dry bias
from pervious
studies
% 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)
LASE-Dropsonde Comparisons (<75 km & <75 min)
• Lear dropsondes were in
good agreement overall
(<5%), but Falcon
dropsondes were
consistently drier by ~8%.
Courtesy Ed Browell,
NASA/LARC
5
H2O DIAL
Dropsonde
Altitude [km]
4
June 9, 2002, 12:51 UT
3
100.21W, 36.56N
2
Altitude [km]
5
DLR-DIAL Comparisons
with Dropsondes
+
4
H2O DIAL
Dropsonde
June 9, 2002, 13:17 UT
3
102.71W, 36.55N
2
1
6
8
10
12
H2O Mixing Ratio [g/kg]
14
1
2
4
6
8
10
12
H2O Mixing Ratio [g/kg]
Courtesy Gehard Ehret (DLR)
14
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.
Data from two field experiments
1. IHOP_2002 (SGP, May-June 2002):
•
71 pairs of co-incident dropsonde and
radiosonde soundings for intercomparisons,
•
Comparisons of old and young sensors.
2. DYCOMS-II (NE Pacific, July 2001):
DYCOMS-II
•
All 63 dropsondes into marine stratocumulus
clouds,
•
Comparisons with co-incident airborne
ascending and descending data.
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.
Norman 1996-2002
VIZ-B
100
VIZ-B2
Vaisala RS80-H
Number of Soundings
90
80
70
60
50
40
30
20
10
0
9601
9701
9801
9901
0001
Year/Month
ARM-B6 1996-2002
0101
0201
June 9, 18 UTC
RH
T
Q
Mean Differences (Dropsonde-Radiosonde)
RH
T
Q
Heat conduction
to explain the cold bias
The bulk-head and sensor boom
are warmer than the environment,
so conduct heat to the sensors:
1. Inside
2. outside
3. reach equilibrium
Tm > Ta and RH2 < RH1
RH2
Sensors come from colder to
warmer air, so sensors lose heat
to the BH/SB :
Tm < Ta and RH1-RH2
4. in the flight
Colder dropsonde T than
radiosonde in IHOP (~0.4C)
RH1
T
IHOP Lear dropsonde PTU sensors (402)
350
297
300
Number of sondes
Ages of PTU
sensors for IHOP
250
200
150
100
48
49
50
Sonde built dates:
8
0
1999
2000
2001
2002
Manufacture Year
Feb-Apr 2002
IHOP Falcon dropsonde PTU sensors (91)
100
88
90
Number of sondes
80
70
60
50
40
30
20
10
1
0
2
1999
2000
2001
0
Manufacture Year
2002
Comparisons of old
and new dropsondes
<20 km, < 40 min
Performance in Clouds (Dycoms-II)
Marine Stratus Cumulus clouds
Specifications of different sensors
during DYCOMS-II
Instrument
Variables
Range
Precision
Accuracy
RH
0-100%
1%
2%*
pressure
temperature
wind
1080-3 hPa
-90C to 60C
0-200 m/s
0.1 hPa
0.1 C
0.1 m/s
0.4 hPa*
0.2 C*
+0.5 m/s
NCAR Lyman-alpha hygrometer
(“stub” and cross-flow)
mixing ratio
0.1-25 g/m3
0.2%
5%
GE 1011B Dew Point
Hygrometer
dew point
temperature
-65C to 50C
0.006C
0.5C (>0 C)
1.0 C (<0 C)
Rosemount temperature sensor
temperature
-60C to 40C
0.006C
1.0C
PMS Liquid Water Sensor
liquid water
content
0-5 g/ m3
0.001 g/m3
0.02 g/m3
Vaisala Dropsonde RD93:
H-HUMICAP thin film capacitor
BAROCAP silicon sensor
THERMOCAP capacitive bead
Codeless GPS receiver GPS 121
Matching dropsonde with C-130
ascending/descending profile
Overshooting
Descending
Ascending
Time-lag Error
Mean estimated time
constant of ~5 s is
larger than 0.5 s
given by the
manufacture.
Sensor Wetting
Introduce alternative heating of
twin humidity sensors to speed up
the evaporation
Performance of the Temperature Sensor: Wetting Error?
Wetting error in airborne insitu T sensors (e.g. Eastin
2002): ~1-3C for Rosemount.
Summary on Dropsonde Evaluation
1. Dry Bias: No systematic dry bias is found in dropsonde humidity data as
suggested by previous studies.
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: 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: The dropsonde humidity sensor still reported nearsaturation 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.