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Validation and use of IASI level2
data in various weather situations
Jana Čampa, Benedikt Strajnar, Mateja Iršič Žibert, Jure Cedilnik, Boštjan Muri
ARSO, Slovenian Environment Agency, Ljubljana
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2 January 2018 in Ljubljana...
ARSO webcam
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Temperature inversion
2 January 2018 in Ljubljana... … and on Krvavec
©Jaka CerarARSO webcam
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Convection
Poelmann et al., 2016
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Can we use IASI level2 profiles directly in operational
forecasting to improve forecasts in these situations?
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Data – Mode-S MRAR temperature
Mode-S: Secondary air traffic surveillance radar process withselective communication
MRAR: Meteorological Routine Air Report (reported by ~5% of allMode-S equipped aircraft)
• Reported data: wind speed, direction, temperature, turbulence, wind shear, microburst, icing
• T and wind speed measured directly (as opposed to Mode-S EHS)
• Data acquisition rate: 4 s
• Radar range at Ljubljana airport: 250 km
• Reported T error (location + measurement error): -0.7 - +0.5°C (Strajnar, 2012)
• Available in the vicinity of Slovenian and Czech air space
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Data – AMDAR temperature and humidity
Aircraft Meteorological DAta Relay –
WMO program to collect met. data from commercial airplanes
• Worldwide > 700,000 wind and T observations, > 11,000 humidity
observations daily
• In Europe 10 Lufthansa aircraft equipped with humidity sensors (~
3000 observations daily)
• Data acquisition rate: 20 s, switched off above 5.5/10 km
• Estimated T error: 0.4 °C, systematic errors of up to 1°C reported
• Humidity sensing limit 0.02g/kg (Hoff 2010, Strajnar 2012)
• Profiles from Venice, Rome, Vienna, Budapest, Munich and
Frankfurt airport selected
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Data – IASI level2 temperature and humidity
• 01 January – 10 December 2017
• Resolution: 12-39 km, 101 vertical levels
• 4-6 overpasses per day (08:15 – 10:15 am, 6:30 – 8:30 pm)
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Data preprocessing
• Temporal smoothing (min 20 s)
• Quality control
Whitelist generated from comparison of Mode-S data with
operational NWP over a period of 22 months (B. Strajnar)
• Selection of the nearest pressure level (Mode-S) /
Interpolation to IASI levels (AMDAR)
• IASI scan selection in the vicinity of mentioned airports
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Data collocation
01 August ~9:30am
IASI scan locations
White: cloudy, green: clear sky
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Data collocation
01 August ~9:30am
IASI scan locations
White: cloudy, green: clear sky
AMDAR profile locations
(8:30 – 10:30 am)
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Data collocation
01 August ~9:30am
IASI scan locations
White: cloudy, green: clear sky
AMDAR profile locations
(8:30 – 10:30 am)
Collocation criteria:
Hor. distance: max 50 km
Time difference: 30 min (Mode-S)
1h (AMDAR)
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Example profiles
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IASI vs. Mode-S temperature
Pre
ssu
re(h
Pa)
T difference and St dev (°C)
N = 666
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IASI vs. Mode-S temperature
Pre
ssu
re(h
Pa)
T difference and St dev (°C)
N = 122N = 242N = 202N = 100
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IASI vs. AMDAR T and Td
T difference and St dev (°C) Td difference and St dev (°C)
Pre
ssu
re(h
Pa)
N = 1268
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IASI vs. AMDAR temperature
Pre
ssu
re(h
Pa)
T difference and St dev (°C)
N = 229N = 402N = 383 N = 254
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IASI vs. AMDAR dew point temperature
Td difference (°C)
Pre
ssu
re(h
Pa)
Td difference and St dev (°C)
N = 383 N = 254 N = 402 N = 229
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19 /
Specific weather situations
Temperature inversion
• Definition: one occurrence of a positive vertical T gradient
between 930 and 700 hPa (dT/dh > 0.)
Convection
• K-index:
K = T850 + Td850 – T500 – (T700 – Td700)
• Showalter index:
SWI = Tenv_500 – Tparcel_500 (lifted from 850 hPa)
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Inversion IASI vs Mode-S
dT/dh_modes > 0. IASI YES IASI NO
Mode-S YES 15 263
Mode-S NO 2 386
dT/dh_modes > 5. IASI YES IASI NO
Mode-S YES 8 5
Mode-S NO 9 644
POD = 0.05
POD = 0.62
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Inversion IASI vs AMDAR
dT/dh_amd > 0. IASI YES IASI NO
AMDAR YES 69 457
AMDAR NO 3 739
dT/dh_amd > 5. IASI YES IASI NO
AMDAR YES 15 0
AMDAR NO 57 1196
POD = 0.13
POD = 1.0
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Convection IASI vs AMDAR
SWI < 0 IASI YES IASI NO
AMDAR YES 40 28
AMDAR NO 10 784
K > 30 IASI YES IASI NO
AMDAR YES 65 82
AMDAR NO 123 592
POD = 0.59
POD = 0.44
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Summary
Mean T diff < 1°C
Mean Td diff < 2.5 °C
Seasonal dependency
(different weather
situations)
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Summary
Limited performance in
inversion situations
(better for pronounced
inversions)
Mean T diff < 1°C
Mean Td diff < 2.5 °C
Seasonal dependency
(different weather
situations)
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Summary
Rel. high hit rates on
K and SWI index
Some false alarms due
to moist bias
Limited performance in
inversion situations
(better for pronounced
inversions)
Mean T diff < 1°C
Mean Td diff < 2.5 °C
Seasonal dependency
(different weather
situations)
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Convection IASI vs AMDAR
SWI < 0 IASI YES IASI NO
AMDAR YES 40 28
AMDAR NO 10 784
K > 30 IASI YES IASI NO
AMDAR YES 65 82
AMDAR NO 123 592
SWI < 3 IASI YES IASI NO
AMDAR YES 311 122
AMDAR NO 85 344
K > 20 IASI YES IASI NO
AMDAR YES 238 95
AMDAR NO 164 365