4th Workshop of the International Precipitation Working Group Beijing, 13-17 October, 2008.

Observing precipitation with AMSU-B opaque channels: Observing precipitation with AMSU-B opaque channels: the 183-WSL algorithmthe 183-WSL algorithm

CNR - Institute of Atmospheric Sciences and Climate, Bologna (Italy)(s.laviola@isac.cnr.it)

Sante Laviola and Vincenzo Levizzani

Outline …

• The Algorithm 183-WSL

• Probability Of Rain Development Function (PORDF). An improvement

to the 183-WSL algorithm

1- Tools

• Saharan dust: red rain over Bulgaria. The 183-WSL module performances

• Severe storm over Italy. 183-WSL vs GPROF/AMSR-E

• Tropical cyclone: the hurricane Dean. 183-WSL vs GPROF/TMI

2- Case studies

4- Conclusion

3- Probability Of Rain Development Function: A better delineation of the 183-WSL light-rain areas

We propose… …a new passive microwave algorithm named 183-WSL (Laviola-Levizzani, 2008) to…

• Estimate precipitation intensities

• Classify convective and stratiform rain

Balcanic Cyclone over Ukraine and Black Sea on 24 July 2008 at 2344 UTC

MSG-RGB AirMass 183-WSL rain rates

… and an improvement to the 183-WSL performances using the Probability Of Rain Development Function.

The algorithm 183-WSL

• Atmospheric windows more used for surface studies: 23.8 – 50 GHz

• Temperature profile: 50 – 60 GHz (O2 band)

Absorption frequencies < 60 GHz

Scattering frequencies > 60 GHz

• Atmospheric windows used for rain, cloud liquid water and ice cloud detections: 89 – 150 GHz

• Humidity profile and rain: 183.31 GHz (water vapor band)

The algorithm 183-WSL is an emission algorithm using the emitted radiation at 183.31 GHz to derive rain rates.

Ch1: 89.0 GHz (W)

Ch2: 150 GHz (W)

Ch3: 183.31+/-1 GHz (WV)

Ch4: 183.31+/-3 GHz (WV)

Ch5: 183.31+/-7 GHz (WV)

Across-track scanning

With AMSU-B frequencies:

Humidity profiles (WV)

Snow cover (W) / Condensed vapor (W/WV)

Rainy/No-rainy cloud systems (WV/W)

Rain type (stratiform/convective) (WV/W)

Rain phase (ice/liquid) (WV/W)

AMSU-B frequency channels

AMSU-B channel characteristics: Mesoscale Convective System over Sicily

Mesoscale Convective System over Sicily as seen from the Advanced Microwave Sounding Unit high window (top) and opaque (bottom) frequencies. The scattering from large ice crystals depresses the incoming radiation

at 150 GHz (b) of about 100 K. On the other hand, the warm system into the black square in (a) absorbs more at 89 GHz than at 150 GHz. Moving from the center to the border of the water vapor band ((c) to (e)) scattering

and absorption combined effects and the vertical development of the convection can be observed (the weighting function of (c) peaks at about 8 Km)

MCS over Southern Italy NOAA-15 150 GHz - 22 October 2005

b

MCS over Southern Italy NOAA-15 184 GHz - 22 October 2005

c

MCS over Southern Italy NOAA-15 186 GHz - 22 October 2005

d

MCS over Southern Italy NOAA-15 190 GHz - 22 October 2005

e

MCS over Southern Italy NOAA-15 89 GHz - 22 October 2005

a

The 183-WSL algorithm approach is based on the conversion of the radiation emitted from a precipitating system into rain rate intensity.

The 183-WSL algorithm: physical approach

Rain rate is inferred by exploiting the perturbation induced by rain drops onto radiation in the strong water vapor absorption band at 183.31 GHz.

Since radiances at 183.31 GHz are strongly affected by temperature and humidity profiles a series of thresholds are computed to remove no-rain signals due to the absorption of

condensed water vapor and scattering of snow on mountain tops.

21 3 4

1 Ingestion / Processing / Land-Sea discrimination

2 Module 183-WSLWV183-WSLWV: Water vapor/Snow cover filtering

Land: ΔT = (BT89-BT150) < 3 K

3 Modules 183-WSLC/S183-WSLC/S: Rain classificationStratiform threshold:

Sea: ΔT = (BT89-BT150) < 0 K

Convective threshold: ΔT > 10 K

4 Module 183-WSLRR183-WSLRR: Rain rate estimation (mm h-1)

The 183-WSL algorithm: retrieval scheme

Sea: 0 < ΔT < 10 K

Land: 3 < ΔT < 10 K

The 183-WSL algorithm: rain/no-rain thresholds

Images below describe the red rain event over Bulgaria on 23 March 2008 caused by a strong Saharan Dust. As seen in (c), only more optically thick clouds (b) are flagged

as precipitating by rain/no-rain thresholds. In (a) RGB MODIS image.

Rain/no-rain thresholds are based on the combination of absorption and scattering effects at 89 GHz and 150 GHz. Experimentally, we found that when brightness

temperature differences (89 - 150) are lower than 3 K no-rain clouds are present into the FOV and therefore removed. Nevertheless, since over cold background (as sea) atmospheric parameters can contaminate particularly the measurement at 89 GHz a

lower threshold has been introduced.

a bc

Case-1: Saharan dust causing red rain over BulgariaOn 23 and 24 May 2008 an intense dust plume from Sahara flying over Greece and the Black Sea interacted with an Atlantic Front generating a persistent red rain over Bulgaria (white arrows). The strongly scattering but non-precipitating particles (water vapor and dust the over Mediterranean) are filtered out by the computational scheme. The incoming Atlantic Front generates deep convection over Italy where rain rate estimations are around 10 mm h-1. From a) to f): MODIS-RGB, MODIS-COT, 183-WSL rainfall, 183-WSLC, 183-WSLS and 183-WSLW. On the top-right, rain intensities increase with scattering signature. (dots are rain 1-5 mm h-1, empty dots rain > 5mm h-1). On the middle-right, rain distribution with longitudes, and on the bottom-right rain types on the basis of classification thresholds.

a bc

d e f

Case-2: Severe storm over Italy: 183-WSL vs GPROF/AMSR-EDuring the severe storm of June 2007 we have tested the 183-WSL performances both

in convective case and moderate rain. Light rain was not frequent.

Fig.3- 11 June at 1149 UTC

18

3-W

SL

GP

RO

F/A

MS

R-E

The 183-WSL underestimates rainfall with respect to GPROF/AMSR-E. Observing discrepancy graphs (vertical bars), an increasing displacement is noted with increasing rain intensities. This is possibly due to the different

nature of the algorithms. In the case of moderate rain (fig.1) precipitating areas are quite similar on the southern Mediterranean Sea; on the north GPROF drastically underestimates and this is true for the other cases as well.

Note that convective system coming from SE is well described but 183-WSL precipitation is more smoothed from the convective core to the borders. In case of lighter rain (fig.3), the 183-WSL describes more rainy areas than

GPROF/AMSR-E, especially over the Alps.

18

3-W

SL

Fig.1- 02 June at 1156 UTC

GP

RO

F/A

MS

R-E

Fig.2- 04 June at 1143 UTC

18

3-W

SL

GP

RO

F/A

MS

R-E

Case-3: Hurricane Dean: 183-WSL vs GPROF/TMI

The cyclone Dean was a classic seasonal tropical system forming over the Cape Verde islands, passing close to Jamaica and pouring rain on the coast of the Yucatan as a category 5 hurricane. Figures below show the cyclone development stages retrieved by the 183-WSL algorithm (left) and the comparison with more coincident TRMM passes (right). Comparing the TRMM-TMI and 183-WSL results a reasonable agreement can be observed.

183-WSL TRMM-TMI

18 at 1404 UTC

19 at 2236 UTC

20 at 0918 UTC

21 at 1615 UTC

22 at 0334 UTC

Hurricane trajectory with the 183-WSL

Probability Of Rain Development Function: A better delineation of the 183-WSL light-rain clouds

An improved version of the 183-WSL algorithm is being developed for a better delineation of the rainy areas. The basic concept is that more pixels associated with high values of condensed water vapor, particularly during light rain events, can be associated

to clouds in a growing stage depending on their water vapor amount. With increasing drop size the condensed water vapor particles can develop into light stratiform rain. To describe this process a Probability Of Rain Development Function (PORDF) has been

set up.

183-WSL no-PORDF

183-WSLW no-PORDF

PORDF

183-WSL improved

183-WSLW improved

Highest probability pixels

183-WSL performances with PORDF improvement

… NO-PORDF

WITH PORDF

Stratiform rain over Belgium

… NO-PORDF

WITH PORDF

Red rain over Bulgaria

Dusty rain regions with PORDF are more delineated than without it. On the left, note the truncated system over Bulgaria and the Black Sea. On the right, PORDF properties complete the missing clouds structure over the Adriatic Sea.

Quasi-pure stratiform rain with mean rainfall rate ~ 3 mm h-1 over Belgium. Note the precipitating field structure better described with PORDF. It can be realistically assumed that the surrounding clouds (top) are water reservoirs that contribute to increase the condensation into the cloud core (green).

…about PORDF

PORDF is being activate when discarded pixels, classified as condensed water vapor (i.e. no-rainy), correspond to rainrates > 2 mm h-1. This limit value is considered as a

crucial threshold between large water vapor particles (non precipitating) and the beginning of stratiform rain development (light precipitation). The 2 mm h-1 threshold

is considered valid at mid-latitude and tropics. At higher latitudes (> 50°), characterized by light or very-light rainfall (often < 2-3 mm h-1), the use of PORDF can

underestimate rainrates.

PORDF develops on the concept of evolutional logistic model where the

starting element of the population are pixels with values greater than 2 mm h-1.

The coefficients of the PORDF model were previously calculated using an ad hoc

model which links observed IR rainclouds (200 K< BT< 253 K) and 183-WSLW

retrieved values.

The drawback of the 183-WSL is its direct link with the atmospheric water vapor amount when condensation clusters are formed both in cloud free regions and in the surroundings of rainy clouds but they are not directly involved in the precipitation process.

Our studies have shown that absorption due to the water vapor contaminates the algorithm estimations labeling as rainy those pixels for which only water vapor absorption was detected. In order to prevent these incorrect rain-flags we calculate a series of thresholds to evaluate only the water vapor contribution, to classify rain types, and to estimate precipitation intensities for each class.

Present results encourage us to apply the method to precipitating events characterized by different stratiform and convective components and by different amounts of water vapor for a better understanding of the algorithm performances. An improvement of rain delineation in the winter season and at latitudes higher than 60° is also needed.

Conclusions 1/2: about 183-WSL algorithm

The comparisons with GPROF/AMSR-E have shown a general underestimation of the 183-WSL rain intensities. Nevertheless, being different the basic concept of two techniques, the global analysis on 15 days of data describes a substantial GPROF/AMSR-E underestimation especially over strong scattering surface (Alps snow cover).

The comparison with GPROF/TMI has shown a good correlation with the 183-WSL results. Nevertheless, the poor dataset, due to the rare overlapping between NOAA and TRMM satellites, does not allow a more complete analysis.

The introduction of PORDF, although an experimental approach needing further validation, gave us encouraging results as regards to a better delineation of the rainy areas surrounding precipitating clouds. These regions, crucial for cloud development and often characterized by light rain, can be considered as sort of reservoirs of accreting particles that supply rain drops to the cloud core.

Conclusions 2/2: about the applications