Reference quality upper-air measurements: GRUAN data

Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92

radiosonde

Ruud Dirksen, Michael Sommer, Franz Immler, Dale Hurst, Rigel Kivi, Holger Vömel

GRUAN RS92 data product CIMO TECO 27-30 September 2016 www.gruan.org

Introduction

• Globally: 800 radiosoundings/day

High-res profiles of important ECVs (PTU, wind)

• Climate research requires reference quality data

Requirements for reference data (Keynote presentation G. Bodeker, Tuesday)

GCOS Reference Upper Air Network (2008)

• Traceability to an SI unit

• Uncertainty analysis

• Raw + meta-data

• Documentation

• Validation

Establishing reference quality

Best estimate+

Uncertainty

Uncertainty of input data

Traceable sensor

calibration

Transparent processing algorithm

Disregarded systematic effects

Black box software

Proprietary methods

GRUAN data product for RS92

Good performance, employed at 50% of GRUAN sitesVaisala calibration of PTU sensors traceable to SI standards

Proprietary algorithms

• Characterization & correction of instrument errors/biases• Vertically resolved error estimates• Well-documented

Error sources for: Temperature HumidityRadiation

Sensor time-lag

Calibration

Vaisala RS92 radiosonde

• PTU, wind

• 0-35km

• Interval: 1 s

• Ascend rate 5 m/s

• 2 RH sensors, alternate heating

• Capacitive sensors (T, U)

• Reflective, hydrophobic coating

T-sensor

RH-sensors

P-sensor

Radiation error: Laboratory experiments

Shadow RS92 records background temperature & ambient pressure

Simultaneous testing of 3 radiosondes

p=[3 hPa , ambient]

Difference illuminated – background radiosonde

Parameterisation of sensor response

Radiation error correction model

Actinic flux on sensor

• LUT actinic flux

• Streamer model, I+D

• Clear sky & cloudy

• Mid-lat atmosphere

T-correction profile

In processing: average of Vaisalaand GRUAN correction• Update in version 3

Temperature: uncertainties

Dominant uncertainty:Sensor orientation wrt Sun

RS92 dual soundingA

Night (N=17) Day (N=29)

Estimated uncertainty vs observed differences

RH: Dry biases

Heating of humidity sensor

• ∆T: radiation correction of T-sensor

• f: enhancement factor [6, 13] (laboratory experiments)

Uncertainties: ∆T, f

Temperature-dependent dry bias (-30, -70°C)

Based on RS92 - CFH comparison (empirical)

Max at 7% at -60°C (similar to Voemel, 2007)

Uncertainty: comparable to correction

RH: sensor time-lag

Low-pass filter

Flattens features in humidity profile

Relevant below -40°C (tropopause)

Correction: numeric inversion + a-posteriori filtering

RH: corrections & uncertainties

Dominant uncertainties:•Calibration•Cal. correction•Dry bias

IPW: ~4%

RS92: reliable RH profiles up to UTLS

Yangjiang 20 July 2010

Manufacturer’s ground check

RS92 recalibration at 0%RH.

Quality of desiccant

Introduces bias

Lindenberg

Ground check in SHC

• Traceability• 4% change over ~9

years• SHC readings enter

uncertainty budget• Future: use SHC to

scale profile

Lindenberg

Outlook

• Improve radiation correction

• Redesign architecture processing software (modular, versatile)

• Develop GRUAN data products for other radiosondes

• Change management RS92-RS41 transition P1(11)

Reference quality upper-air measurements: GRUAN data

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