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 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) 1.6% increase in global precipitation in the last 140 years from rain gages that cover about 30% of land surfaces globally Obvious need for remote sensing for both applications

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 AVHRR 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 273 K isothermal typically at 2-3 km 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* 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: 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: night: additional information from 11 µm-12 µm: PIday=a*Tsurf-b*T11+c*ln(R0.6/R3.8) PInight=a*Tsurf-b*T11+c*(T11-T12)

+ high spatial resolution + convective cells, even small ones, AVHRR + high spatial resolution + convective cells, even small ones, 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 AMSU - low spatial resolution - small convective cells 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