Inferring Convective Weather
Characteristics with Geostationary
High Spectral Resolution IR Window
Measurements: A Look into the
Future
Justin Sieglaff and others
CIMSS/SSEC
Cooperative Institute for Meteorological Satellite Studies
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Acknowledgements
ƒ Co‐Authors on Journal Article
à Tim Schmit – NOAA/NESDIS/STAR
à Paul Menzel – UW/CIMSS
à Steve Ackerman – UW/CIMSS
ƒ Louie Grasso – CIRA/CSU for RAMS temperature and moisture profile data
ƒ Article Submitted to JTECH
à Recently responded to reviewer feedback
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Objectives
1. Background/Introduction
2. Demonstrate sensitivity of water vapor and carbon dioxide absorption lines within infrared window to thermodynamic variability in the lower troposphere
3. Apply findings to current challenge in the field: Lapse rate and moisture trends for use in short‐term convective forecasting
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Introduction
• Water vapor and carbon
dioxide lines within the
infrared window are
sensitive to the thermodynamic state of the
lower troposphere
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Motivation
Johns and Doswell (1992) to achieve deep convection there must be…
1.
2.
3.
A moist layer of sufficient depth in low or mid troposphere
A steep enough lapse rate to allow for substantial ‘positive area’
Sufficient lifting of a parcel from the moist layer to allow it to reach its level of free convection
Johns and Doswell (1992), “The challenge [of the forecaster] is to deduce the structure between observations in space and time, utilizing the limited sounding data…”
Doswell (2001), “The importance of the thermodynamic structure…for deep moist convection processes cannot be underestimated. …
Satellite‐borne remote sensors continue to improve and … the high temporal resolution capability afforded by the current and future generations of meteorological satellites offer much promise.”
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Data and Methods
ƒ A GEO high spectral resolution IR sounder does not currently exist (in orbit); this requires use of simulated GEO high spectral measurements
ƒ Simulated radiances were from a fast forward model based on the GIFTS Forward Model, run at 0.625 cm‐1 (noise‐free).
ƒ Input T and q profiles are from CSU/RAMS model for a point over central Kansas on 08 May 2003.
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Terminology
Off‐line region
On‐line region (absorption feature)
Absorption Feature Magnitude
AFM = TBBoff −line − TBBon−line
Time Difference AFM
TDAFM
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AFM t 2
AFM t1
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What about skin temperature?
ƒ An unrealistic, yet key assumption in the following simulations is the skin temperature and lowest tropospheric temperature difference is held constant.
ƒ If not the absorption feature magnitudes (right) would largely be a measure of skin surface temperature change.
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Lapse Rate Sensitivity
Simulation with varied lapse rate, constant moisture
Water Vapor and Carbon Dioxide
TDAFMs
CO2 and H2O TDAFMs have similar sensitivity to changes in lapse rate (value ~1.5K)
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Low-level Moisture Sensitivity
Simulation with varied moisture, constant temperature
Water Vapor and Carbon Dioxide
TDAFMs
CO2 and H2O TDAFMs have different sensitivity to changes in moisture
CO2 TDAFM: 0.48K H2O TDAFM: ‐0.52K
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Full Thermodynamic Sensitivity
Varied Temperature and Moisture
Water Vapor and Carbon Dioxide
TDAFMs
CO2 and H2O TDAFMs show changes in lapse rate and moisture are distinguishable CO2 TDAFM: 1.98K H2O TDAFM: 1.07K
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Apply Sensitivity to Forecasting
Lapse Rate and Moisture Time Trends
Lower Tropospheric Lapse Rate Product
∂LapseRate ∂CO 2 AFM
=
= TDAFM CO 2
∂t
∂t
Lower Tropospheric Moisture Product
∂Moisture ∂CO2 AFM ∂H 2OAFM
= TDAFMCO2 − TDAF M H 2O
=
−
∂t
∂t
∂t
∂LapseRate
= CO 2TDAFM < 0
∂t
Lapse Rate decreased
Moisture
; TDAFM H 2O
t
TDAFM CO 2
Moisture increased
∂LapseRate
= CO 2TDAFM = 0
∂t
No change Moisture
; TDAFM H 2O
t
TDAFM CO 2
No change
∂LapseRate
= CO 2TDAFM > 0
∂t
Lapse Rate increased
Moisture
; TDAFM H 2O
t
TDAFM CO 2
Moisture decreased
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Apply Sensitivity to Forecasting
Example using IASI – 16 March 2008
Using LEO IASI:
Take two points across a synoptic boundary; serves as a proxy for temporal change at one point
CO2 TDAFM: ‐2.8K
H2O TDAFM: ‐5.5K
(at 1244 cm‐1)
Negative CO2 TDAFM suggests decreasing lapse rate from M to D
H2O TDAFM < CO2
TDAFM suggests decreasing moisture from M to D
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Comparing Current GOES to High
Spectral Measurements
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Comparing traditional GOES Sounder split window temporal trend (red line) versus water vapor TDAFMs (blue lines).
Low‐level moisture is decreasing with time:
1700 UTC: 33.0mm
1730 UTC: 32.0mm
1800 UTC: 29.4mm
1830 UTC: 25.9mm
GOES Sounder split window varies by ~0.1K over the 90 minute period.
High spectral water vapor TDAFMs vary by 0.4 to 0.6K over the 90 minute period.
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High spectral measurements have 4 to 6 times more sensitivity to changes in low‐
level moisture than over current GOES Sounder. 15
Conclusions
ƒ Water Vapor and Carbon Dioxide absorption lines within the infrared window are sensitive to changes in the lower tropospheric thermodynamic state
ƒ Current GOES sounders are spectrally too broad to resolve these lines
ƒ Information obtained from a high spectral GEO sounder would be very useful for monitoring pre‐convective clear sky regions
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Comments/Future Work
ƒ Need to develop technique to account for changing surface skin temperature
ƒ Technique presented is not intended to replace full retrieval of T and q (and derived products such as LI, CAPE,TPW, etc.)
à This technique is computationally inexpensive and offers rapid turn around time
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