Precipitation Classification and Analysis from AMSU
Ralf Bennartz
University of Kansas
Lawrence, KS
Anke Thoss, Adam Dybbroe, Daniel B. Michelson
Swedish Meteorological and Hydrological Institute
Norrköping, Sweden
Work performed within EUMETSAT Nowcasting SAF
Outline
Introduction
Method overview and statistical significance
•AMSU
•AVHRR
•combining AMSU and AVHRR
Case Studies
Outlook
Demands on precipitation estimates
 Climate research
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High absolute accuracy needed to detect climate signals
Global observations
Temporal and spatial averages
Long term observation
Monitoring of extreme events
 Nowcasting/short range forecasting
• Timely product generation
• Best possible spatial resolution
• Absolute accuracy not of major importance (three to four
intensity classes are sufficient)
The data set
• Eight month of NOAA-15
AMSU-A/B and AVHRR
(April 99-November 99)
• Eight month of NOAA-16
AMSU-A/B and AVHRR
(February 01- September 01)
• Co-located BALTEX-radar Data
Centre radar data for the entire
Baltic region, 25 radars, gauge
adjusted
AMSU-A/B
• conically scanning microwave radiometer
• spectral range 23-190 GHz, channels used:
23 GHz, 89GHz, 150GHz
• 3.3 degree resolution AMSU-A
• 1.1 degree resolution AMSU-B
AVHRR
• channels used: 0.6 m, 3.7 m, 11 m and 12 m
• 1km resolution at nadir ( 0.054 degree )
Observation geometry
Altitude of radar beam
(elevation 0.5°):
@100km distance: 2.2 km
@200km distance: 5.2 km
273 K isothermal
typically at 2-3 km
Four classes of precipitation intensity from
co-located radar data
Rain rate
Class 1:
Precipitation-free
0.0 - 0.1 mm/h
Class 2:
Risk for precipitation
0.1- 0.5 mm/h
Class 3:
Light/moderate precipitation
0.5 - 5.0 mm/h
Class 4:
Intensive precipitation
5.0 - ... mm/h
Passive microwave precipitation signal
• Most directly linked to
surface precipitation
• Over cold (water) surfaces
only
• Works over both land and
water surfaces
• More indirect
The scattering index
• Predict brightness temperature T* in absence of scattering
from low frequencies (functional relation is found by
inverse radiative transfer modelling or global brightness
temperature statistics)
• Take observed high frequency brightness temperature
and subtract T*
SI  Tobserved  T * Tlowfreq 
• Has been found to be a linear measure for precipitation
intensity
AMSU-A water or coast, AMSU-B land:
SI= T89-T150-CORR
AMSU-A land (and AMSU-B land):
SI= T23-T150-CORR
AMSU-B water:
SI= T89-T150-CORR
correction factor CORR corrects for scan position effects and statistical
offset for non scattering situations (for SI water adjusted dynamically).
Probability of different classes over sea
AVHRR algorithm:
calculate a Precipitation index PI for day or night
•11µm Tb most important
•day: additional information from R0.6 µm/R3.7 µm:
PIday=a*Tsurf-b*T11+c*ln(R0.6/R3.8)
•night: additional information from 11 µm-12 µm:
PInight=a*Tsurf-b*T11+c*(T11-T12)
AVHRR
+ high spatial resolution
+ convective cells, even small ones,
AMSU
- low spatial resolution
- small convective cells
can be well identified
- no strong coupling between
spectral signature and rain
- area of potential rain overestimated
generally low likelihood
- intensity and likelihood not really
decoupled
sometimes missed
+ stronger coupling between
rain and scattering signature
+ rain areas better delineated
+ more independent intensity and
likelihood information
- sometimes spurious light rain
- not applicable over snow and ice
Combining AVHRR and AMSU
AVHRR mainly used for QC of AMSU:
•run cloud type analysis
•for AVHRR pixels containing a potentially raining
cloud type compute precipitation likelihood
•if total precipitation likelihood from AVHRR > 5%*
take replace precipitation estimate with AMSU estimate
(if available)
•over snow and sea ice use AVHRR only
*thresholidng with a 5% likelihood from AVHRR has the
effect that about 5% of the rain according to (imperfect)
radar estimates are missed.
NOAA15 overpass 13 September 2000, 06:43 UTC
RGB AVHRR ch3,4,5 PC product RGB:
red: very light
green:light/moderate
blue:intense
Radar composite
NOAA15 overpass 13 September 2000, 05:48 UTC
RGB AVHRR ch3,4,5 PC product RGB:
red: very light
green:light/moderate
blue:intense
Radar composite
NOAA15 overpass 13 September 2000, 06:43 UTC
RGB AVHRR ch3,4,5 PC product RGB:
red: very light
green:light/moderate
blue:intense
Radar composite
different projection!
NOAA15 overpass 13 September 2000, 05:48 UTC
RGB AVHRR ch3,4,5 PC product RGB:
red: very light
green:light/moderate
blue:intense
Radar composite
different projection!
Conclusions
 Empirical approach to detect precipitation and to classify
intensity for use in nowcasting
 User is provided with the likelihood of four different classes of
precipitation intensity
 150 GHz gives more detail especially over land surfaces
 AVHRR good for QC of AMSU data
Outlook
 Extend to different climates (work in progress for Spain)
 Better incorporation of AMSU and AVHRR
 Extend method to combination of MODIS and AMSU/HSB on
AQUA