Background and Motivation
1
ENHANCEMENT OF SATELLITE-BASED PRECIPITATION ESTIMATES USING THE INFORMATION FROM THE PROPOSED ADVANCED BASELINE IMAGER (ABI), PART I: USE OF MODIS CHANNELS FOR RAIN / NO RAIN SEPARATION Background and Motivation Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD [email protected] Precipitation Estimates from Single-Channel IR Data • Since raining clouds are opaque at IR wavelengths, estimates from IR data relate cloud-top brightness temperature (T b ) to rain rates: • Since temperature decreases with height in the troposphere, lower T b ’s generally indicate higher cloud tops. • Cloud-top height is assumed to be related to updraft strength and moisture flux, and thus to rain rate. • However, these assumptions are violated in many cases, including cold cirrus and warm nimbostratus (Fig. 1). Data and Methodology References Ackerman, S. A., W. L. Smith, J. D. Spinhirne, and H. E. Revercomb, 1990: The 27-28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8-12 m window. Mon. Wea. Rev., 118, 2377-2388. Tjemkes, S. A., L. van de Bert, and J. Schmetz, 1997: Warm water vapor pixels over high clouds as observed by Meteosat. Beitr. Phys. Atmos., 70, 15-21. Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent clouds by bi-spectral measurements in the 10 micron window region. J. Meteor. Soc. Japan, 63, 88-99. Data Sets • MODIS-Terra data from June- August 2005 were aggregated to 2-km resolution and used as a proxy for five ABI channels: 6.8, 8.5, 11.0, 12.3, and 13.2-µm (Fig. 3). • These data were matched with corresponding WSR-88D reflectivity data. Initial Results and Future Work Precipitation Estimates from Multi-Channel Data • The use of visible data together with IR data to identify (non-raining) thin cirrus was first proposed in the 1970’s. • Since then, techniques have been developed for using differences in channel brightness temperatures to derive cloud and precipitation characteristics. • Physical basis: the emissivity of water in all phases changes with frequency, producing unique signals when pairs of channels are compared (Fig. 2). • The Advanced Baseline Imager (ABI) on the GOES-R series of satellites will offer 16 channels instead of the current 5, offering additional possibilities of extracting information from spectral variations in emissivity. Methodology • The impact of the each channel was evaluated probabilistically: the data were divided into 1-K bins and the probability of precipitation (PoP) was computed for each bin as the ratio of the number of raining pixels (radar reflectivity>5 dBz) to the total number of pixels (see Fig. 3 for example). • The relative skill of the resulting PoPs was evaluated by computing the Brier Score for each PoP table and then computing a skill score (percentage reduction in Brier Score) compared to the baseline PoP table using T 11.0 alone. • To capture the variability of Figure 3. Comparison of spectral response and brightness temperature (standard atmosphere) from corresponding ABI and MODIS channels. (Courtesy Mat Gunshor, CIMSS). Single-Channel Evaluation (Fig. 4) • T 8.5 is a slightly better predictor of probability of precipitation than T 11.0 ,but much of this improvement comes from low PoP 11.0 values becoming even lower PoP 8.5 values for dry pixels. • T 6.8 is the poorest predictor, presumably because water vapor attenuation weakens its relationship with cloud-top temperature. • T 12.0 is presumably poorer than T 11.0 because of greater water vapor sensitivity. Two-Channel Evaluation (Fig. 5) • All four combinations show improvement, but the combination of T 6.8 and T 11.0 is the least consistent of the four, again because of sensitivity to water vapor. • The combination of T 11.0 and T 13.2 had the greatest median skill, but CO 2 sensitivity led to less consistent results. • Next step: complete evaluation on longwave IR data and investigate channels with a significant reflected solar component to take advantage of sensitivity to cloud-top particle phase and size. • Longer term: Extend to shortwave IR and near-IR channels, carefully separating the reflected and emitted components. Nimbostratus T b =240 K 200 250 T (K) Cumulonimbus T b =200 K Figure 1. Illustration of the IR signal produced by different cloud types. Cirrus T b =210 K Relatively warm T 6.8 rain (Tjemkes et al. 1993) Relatively cold T 6.8 no rain T 6.8 vs. T 11.0 T 12.0 vs. T 11.0 Small T 11.0 -T 12.0 thick cloud (Inoue 1985) Large T 11.0 -T 12.0 cirrus Figure2. Probability of precipitation as a function of MODIS brightness temperature values: 6.8- and 11.0-µm (left) and 12.0- and 11.0-µm (right). Two-Channel Classification (Fig. 6) • To evaluate the reason for the improvement, the change in skill score was related to T b differences (Fig. 6). • T 8.5 is a better predictor than T 11.0 when (T 8.5 -T 11.0 )>-1.5 K and that T 12.0 is a better predictor than T 11.0 when (T 11.0 -T 12.0 )>-1 K. • Particularly poor skill is exhibited when (T 11.0 -T 6.7 )<-35 K (i.e., clear air contaminated by water vapor). Jung-Sun Im I.M. Systems Group, Kensington, MD Ralph R. Ferraro NOAA/NESDIS/STAR, Camp Springs, MD MODIS (µm) 13. 4 12. 0 11. 0 8.5 6.8 ABI (µm) 13. 3 12. 3 11. 0 8.5 6.1 ΔT b (K) - 4.1 +1. 4 +0. 1 ~0 +2. 1 DISCLAIMER: The contents of this poster are solely the opinions of the author and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. Government. Acknowledgment: This work was supported in part by the GOES-R Risk Reduction (GOES-RRR) program. 6.7 µm 8.5 µm 12.0 µm 13.2 µm Figure 6. Temperature differences with T11.0 as a function of the SS of the rain/no rain separation. Mean SS % of Positive SS Negative Skill Positive Skill Figure 5. Skill Score (relative to 11.0 µm) for probabilistic rain/no rain separation for several combinations of pairs of MODIS/ABI channels. Mean SS % of Positive SS Negative Skill Positive Skill Figure 4. Skill Score (relative to 11.0 µm) for probabilistic rain/no rain separation for several MODIS/ABI channels.
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% of Positive SS. Positive Skill. Negative Skill. Cirrus T b =210 K. Mean SS. Figure 4. Skill Score (relative to 11.0 µ m) for probabilistic rain/no rain separation for several MODIS/ABI channels. Cumulonimbus T b =200 K. Nimbostratus T b =240 K. % of Positive SS. 200. 250. T (K). - PowerPoint PPT Presentation
Transcript of Background and Motivation
ENHANCEMENT OF SATELLITE-BASED PRECIPITATION ESTIMATES USING THE INFORMATION FROM THE PROPOSED ADVANCED BASELINE IMAGER (ABI), PART I: USE OF MODIS CHANNELS FOR RAIN / NO RAIN SEPARATION
ENHANCEMENT OF SATELLITE-BASED PRECIPITATION ESTIMATES USING THE INFORMATION FROM THE PROPOSED ADVANCED BASELINE IMAGER (ABI), PART I: USE OF MODIS CHANNELS FOR RAIN / NO RAIN SEPARATION
Background and Motivation
Robert J. KuligowskiNOAA/NESDIS Center for Satellite Applications and Research (STAR), Camp Springs, MD
Precipitation Estimates from
Single-Channel IR Data• Since raining clouds are opaque at IR
wavelengths, estimates from IR data
relate cloud-top brightness temperature
(Tb) to rain rates:
• Since temperature decreases with
height in the troposphere, lower Tb’s
generally indicate higher cloud tops.
• Cloud-top height is assumed to be
related to updraft strength and
moisture flux, and thus to rain rate.
• However, these assumptions are
violated in many cases, including cold
cirrus and warm nimbostratus (Fig. 1).
Data and Methodology
ReferencesAckerman, S. A., W. L. Smith, J. D. Spinhirne, and H. E. Revercomb, 1990: The 27-28 October 1986 FIRE IFO cirrus case study: Spectral properties of cirrus clouds in the 8-12 m window.
Mon. Wea. Rev., 118, 2377-2388.Tjemkes, S. A., L. van de Bert, and J. Schmetz, 1997: Warm water vapor pixels over high clouds as observed by Meteosat. Beitr. Phys. Atmos., 70, 15-21.Inoue, T., 1985: On the temperature and effective emissivity determination of semi-transparent clouds by bi-spectral measurements in the 10 micron window region. J. Meteor. Soc. Japan,
63, 88-99.
Data Sets• MODIS-Terra data from June-August 2005
were aggregated to 2-km resolution and
used as a proxy for five ABI channels: 6.8,
8.5, 11.0, 12.3, and 13.2-µm (Fig. 3).
• These data were matched with
corresponding WSR-88D reflectivity data.
Initial Results and Future Work
Precipitation Estimates from Multi-Channel Data• The use of visible data together with IR data to identify (non-raining) thin cirrus
was first proposed in the 1970’s.
• Since then, techniques have been developed for using differences in channel
brightness temperatures to derive cloud and precipitation characteristics.
• Physical basis: the emissivity of water in all phases changes with frequency,
producing unique signals when pairs of channels are compared (Fig. 2).
• The Advanced Baseline Imager (ABI) on the GOES-R series of satellites will offer
16 channels instead of the current 5, offering additional possibilities of extracting
information from spectral variations in emissivity.
Methodology• The impact of the each channel was
evaluated probabilistically: the data were
divided into 1-K bins and the probability of
precipitation (PoP) was computed for each
bin as the ratio of the number of raining
pixels (radar reflectivity>5 dBz) to the total
number of pixels (see Fig. 3 for example).
• The relative skill of the resulting PoPs was
evaluated by computing the Brier Score for
each PoP table and then computing a skill
score (percentage reduction in Brier Score)
compared to the baseline PoP table using
T11.0 alone.
• To capture the variability of Brier Score
from one scene to the next, Tukey box plots
were constructed for each scene.
Figure 3. Comparison of spectral response and brightness temperature (standard atmosphere) from corresponding ABI and MODIS channels. (Courtesy Mat Gunshor, CIMSS).
Single-Channel Evaluation (Fig. 4)• T8.5 is a slightly better predictor of probability of precipitation than T11.0 ,but much of this improvement comes from low PoP11.0 values
becoming even lower PoP8.5 values for dry pixels.
• T6.8 is the poorest predictor, presumably because water vapor attenuation weakens its relationship with cloud-top temperature.
• T12.0 is presumably poorer than T11.0 because of greater water vapor sensitivity.
Two-Channel Evaluation (Fig. 5)• All four combinations show improvement, but the combination of T6.8 and T11.0 is the least consistent of the four, again because of
sensitivity to water vapor.
• The combination of T11.0 and T13.2 had the greatest median skill, but CO2 sensitivity led to less consistent results.
• Next step: complete evaluation on longwave IR data and investigate channels with a significant reflected solar component to take
advantage of sensitivity to cloud-top particle phase and size.
• Longer term: Extend to shortwave IR and near-IR channels, carefully separating the reflected and emitted components.
NimbostratusTb=240 K
200 250T (K)
CumulonimbusTb=200 K
Figure 1. Illustration of the IR signal produced by
different cloud types.
CirrusTb=210 K
Relatively warm T6.8rain (Tjemkes et al. 1993)
Relatively cold T6.8no rain
T6.8 vs. T11.0T12.0 vs. T11.0
Small T11.0-T12.0 thick cloud (Inoue 1985)
Large T11.0-T12.0 cirrus
Figure2. Probability of precipitation as a function of MODIS brightness temperature values: 6.8- and 11.0-µm (left) and 12.0- and 11.0-µm (right).
Two-Channel Classification (Fig. 6)• To evaluate the reason for the improvement, the change in skill score was related to Tb differences (Fig. 6).
• T8.5 is a better predictor than T11.0 when (T8.5-T11.0)>-1.5 K and that T12.0 is a better predictor than T11.0 when (T11.0-T12.0)>-1 K.
• Particularly poor skill is exhibited when (T11.0-T6.7)<-35 K (i.e., clear air contaminated by water vapor).
Jung-Sun ImI.M. Systems Group, Kensington, MD
Ralph R. FerraroNOAA/NESDIS/STAR, Camp Springs, MD
MODIS (µm) 13.4 12.0 11.0 8.5 6.8
ABI (µm) 13.3 12.3 11.0 8.5 6.1
ΔTb (K) -4.1 +1.4 +0.1 ~0 +2.1
DISCLAIMER: The contents of this poster are solely the opinions of the author and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U.S. Government.
Acknowledgment: This work was supported in part by the GOES-R Risk Reduction (GOES-RRR) program.
6.7 µm 8.5 µm 12.0 µm 13.2 µm
Figure 6. Temperature differences with T11.0 as a function of the SS of the rain/no rain separation.
Mean SS
% of Positive SS
Negative Skill
Positive Skill
Figure 5. Skill Score (relative to 11.0 µm) for probabilistic rain/no rain separation for several combinations of pairs of MODIS/ABI channels.
Mean SS
% of Positive SS
Negative Skill
Positive Skill
Figure 4. Skill Score (relative to 11.0 µm) for probabilistic rain/no rain separation for several MODIS/ABI channels.
