Evaluation of Dropsonde Humidity and Temperature

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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)
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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.

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Thanks to James Franklin, NOAA/AOML/NHC
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Humidity dry bias from pervious studies
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• Lear dropsondes were in good agreement overall
  (lt5), but Falcon dropsondes were consistently
  drier by 8.

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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.

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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.

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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.

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June 9, 18 UTC
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Mean Differences (Dropsonde-Radiosonde)
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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 gt
Ta and RH2 lt RH1
1. Inside
2. outside
RH2
3. reach equilibrium
RH1
T
4. in the flight
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(No Transcript)
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Ages of PTU sensors for IHOP
Sonde built dates Feb-Apr 2002
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Comparisons of old and new dropsondes
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Performance in Clouds (Dycoms-II)
Marine Stratus Cumulus clouds
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Specifications of different sensors during
DYCOMS-II
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Matching dropsonde with C-130 ascending/descending
profile
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Time-lag Error
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Sensor Wetting
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Performance of the Temperature Sensor Wetting
Error?
Wetting error in airborne in-situ T sensors (e.g.
Eastin 2002) 1-3?C for Rosemount.
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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.

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Comparisons of old and new sondes (Ocean Waves)
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BAMEX
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Comparisons with co-incident radiosonde data
(BAMEX)
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Sensor wetting (wet-bulb) Rev D sonde? (BAMEX)
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