Elsa Cattani, Francesca Torricella, and Vincenzo Levizzani ISAC CNR (Italy)

3rd IPWG Workshop on Precipitation Measurements - Melbourne, 23-27 October, 2006

Do cloud microphysical parameters derived from daytime multi-spectral satellite observations correlate with rainfall estimates?

Elsa Cattani, Francesca Torricella, and Vincenzo Levizzani

ISAC CNR (Italy) Institute of Atmospheric Sciences and Climate National Research Council - Bologna


Outline

Methods used to analyse the cloud and rain fields

Geographic area and time period

The data

Comparison of TMI and PR rain derived products

The RGB microphysical visualization of the cloud field

Retrieval of cloud properties by means of CAPCOM

Compare cloud microphysics & rain

Conclusions


Methods of analysis of the cloud/rain field

Use of the TRMM payload to analyse the cloud/precipitation field in different spectral regions

Exploit TMI & PR operational rain products

Consider the PR rain field as the truth and statistically compare the rain data (instantaneous rain intensity at the ground in mm h -1)

Derive the cloud mask from multi-spectral observations for the scenarios

Display RGB pictures of the cloud field derived by combining VIS-NIR and IR channels of the VIRS

Derive cloud microphysical parameters by means of a suitable retrieval scheme (CAPCOM)

Produce & compare the maps


Study area and period

West Africa (WA)

- 5 < LAT < 20 - 25 < LON < 25 June 2004 (from 1th to 10th)

Area characterized by clusters of convective precipitation or MCSs

Convection tends to initiate in the lee of mountains and propagates in general direction of prevailing flow

Convection can regenerate through a number of diurnal cycles


TRMM operational products used in the analysis (from GES DAAC) http://daac.gsfc.nasa.gov/www/

Product code Sensor Variables used Resolution swath

2A12 – (swath) TMI Surface rain

Convective rain

5.1 km878 km

2A25 – (swath) PR Near surface rain

Convective/stratiform flag

5.0 km, 250 m (vertical)247 km

1B01 – (swath) VIRS Radiances at 0.6, 3.7 and 11 micron

2.4 km833 km

post-boost


Examples of rain maps from PR and TMI

June 1st, daytime (i.e. SZA < 70°)

mm h-1


Statistical comparison of TMI and PR surface rain data

The original 2A12 and 2A25 rain data are mapped to 0.1° latitude –longitude grid. On average 20 PR and 27 TMI observations in each grid mesh Daytime data: 40 orbits for TMI (34,462 pixels in the common area of the swaths) and 35 PR orbits (108,406 pixels) over the area during the 10 days The rain information from PR and TMI are compared after re-projection onto this grid The whole WA is divided in macro-areasFor each macro-area the analysis is applied separately The PR 2A25 rain data are considered as rain truth data

dlon =0.1°

dlat =0.1°

desertic

wetsea

arid


Definitions from: WWRP/WGNE Joint Working Group on Verification Forecast Verification - Issues

Probability of detection (hit rate)* Answers the question: What fraction of the observed "yes" events were correctly forecast? Range: 0 to 1. Perfect score: 1.

False alarm ratio Answers the question: What fraction of the predicted "yes" events actually did not occur (i.e., were false alarms)? Range: 0 to 1. Perfect score: 0.

Heidke skill score (Cohen's ) where

Answers the question: What was the accuracy of the forecast relative to that of random chance? Range: minus infinity to 1, 0 indicates no skill. Perfect score: 1.

* We used both POD0 i.e. having a zero rain/no-rain boundary and POD1, having 1 mm h -1 rain/no-rain boundary


Perfect score

WA SEA ARID WET

RMSE 0 4.76 3.27 4.99 7.07

Mean RR PR (TMI) 2.41 (2.56) 1.90 (1.57) 2.47 (2.87) 2.70 (4.03)

AB (additive bias)

0 -0.15 0.33 -0.40 -1.33

CORR 1 0.59 0.71 0.60 0.56

POD0 1 0.60 0.59 0.60 0.61

POD1 1 0.57 0.43 0.62 0.65

FAR0 0 0.19 0.30 0.13 0.19

FAR1 0 0.18 0.13 0.19 0.27

HSS0 1 0.69 0.64 0.71 0.70

HSS1 1 0.67 0.57 0.70 0.69

Table of Statisticsbad good


RGB display of VIRS measurements

The RGB technique provides a relatively simple rendering of multispectral satellite information for the meteorological scenario interpretation

The optimum coloring of the RGB image composites rely on the proper selection of the channels and the enhancement of the individual colors

The channel selection must be driven by the particular phenomenon (low or high clouds, dust, smoke etc.) to be emphasized in the satellite image

The proper color enhancement requires the conversion from radiances to brightness temperatures (IR channels) or reflectances (VIS/NIR channels), selection of the display mode (inverted or not inverted), stretching of the dynamic range of the satellite data, and gamma correction

The adopted scheme, so-called day microphysical is particularly recommended for cloud analysis (optical thickness, effective size and top height) and for emphasizing the presence of severe convection


Adopted RGB display: “day microphysical”

From the SEVIRI images interpretation guidelines*, we selected the so-called day-microphysical scheme, developed to interpret and analyze the following components/scenarios:Cloud – Convection – Fog – Snow – FiresAdapted to VIRS, it combines channels this way:R = Channel 01 (VIS0.6)G = Channel 03 (NIR3.7 only reflected solar component)B = Channel 04 (IR10.8)

deep precip cloud

thin Cirrussmall ice particle

severe conv.

thin Cirruslarge ice particle

veget. land

ocean

June 1th

* By, and with the contribution of: H. P. Roesli, J. Kerkmann, D. Rosenfeld, M. KönigAvailable at http://oiswww.eumetsat.org/WEBOPS/msg_interpretation/index.html


Identifying the cloudy pixels

The cloud mask can be described as a cascade of tests involving VIRS channels. It is applied separately over land and sea pixels.Cloudy pixel in the cloud swath are identified by using very conservative tests, already set to identify precipitating clouds by the CERES science team.This kind of selection identify optically thick, ice clouds such as towering cumulonimbus.

Over the landif [TB(CH4) < 257 K] and [R(CH1) > 0.38] it’s a cloud and also [TB(CH3) - TB(CH4) > 20 K] and [TB(CH4) < 237 K] and [R(CH1) > 0.45] it’s raining (maybe!)

Over the sea First avoid sun glint: θscatt > 36° then if [TB(CH4) < 257 K] and [R(CH1) > 0.065] it’s a cloud and also [TB(CH3) - TB(CH4) > 20 K] and [TB(CH4) < 237 K] and [R(CH1) > 0.45] it’s raining (maybe!)


Example of cloud mask map

thick cloud raining cloud

June 1st


Microphysical properties retrieval from VIRS: CAPCOM (Comprehensive Analysis Program for Cloud Optical Measurement)

It allows for the retrieval of cloud optical thickness (), effective radius (Re)

and top temperature (Ttop) from satellite measurements in the VIS, NIR and IR

channels. The cloud phase is supposed known.

Undesirable radiation components (ground reflected and thermal emitted radiation, NIR thermal contribution) are subtracted from the satellite radiance to derive the cloud signal.

The retrieval is performed by means of comparison between the modelled cloud radiances and the corresponding satellite radiance measurements.

The LUTs are built for a grid of selected values of , Re, Ttop, water vapour

amount above and below the cloud layer, solar zenith, satellite zenith and relative azimuth angles. Separate LUTs for ice and water clouds are computed.

Nakajima, T. Y., and T. Nakajima, 1995, J. Atmos. Sci.,52, 4043 – 4059Kawamoto, K., T. Nakajima, and T. Y. Nakajima, 2001, J. Climate, 14, 2054 – 2068

CAPCOM is available at http://www.ccsr.u-tokyo.ac.jp/~clastr/


Current values of c, Re, Tc

Computes Z e PC

Computes LWP, LWC and D

Ancillary data ADT(z), WV(z), P(z)

Computes WeuWecWel

LRTM(0.6) LRTM(3.7) LRTM (11)LSR LTHC LTHG LTHU LTHL

LUTS

Observing geometry

FIRST GUESS of c, Re, Tc

Corrects measurements

[LCORR() - LRTM ()]/LCORR()

Newton-Raphson method

Check the convergenceyes

Compute new current values for c, Re, Tc

i-th iteration

i 10

i > 10: re-computes first guess

c, Re, Tc retrieved =

current values

CAPCOM flow chart

Measured radiances

no

Simulated radiances

indesiderable components to be removed from measurements


Case 1: June 1st, orbit #37307, LT ~ 13:20, rain over the ocean

RGB + PR Cloud mask Effective radius

PR TMI5 µm

14

20

30

40

60

180


Case 2: June 1th, orbit #37306, LT ~ 13:15, rain over land

RGB + PR

Cloud mask

PR TMI5 µm

14

20

30

40

60

180


Case 3: June 3th, orbit #37337, LT ~ 12:40, rain over the coast RGB + PR Cloud mask Effective radius

PR

TMI

5 µm

14

20

30

40

60

180


Conclusions (1/2)

Based on TRMM data, WA daytime convection is analysed in several channels (from VIS to MW) to derive maximum information on rain processes

Cloud mask tests perform very well in this geographic area having such large thermal contrast between cloud top and surface

Cloud structures delineated in the cloud mask nicely agree with RGB images

Rain data derived from TMI and PR show a limited agreement depending also on the climatic sub-area, due to differences in algorithms, resolution, observing geometry, frequency, etc.


The microphysical characterization of the cloud tops (especially in terms of Re) does not add significant information about the underlying precipitation layer: the thick frozen layer detected from the scattering in TMI high frequency channels is detected in multispectral VIRS data as well, but even the greater Re values do not correlate with surface rain

Moreover, due to the input set-up of the microphysical retrieval code, in large areas corresponding to the colder and higher (overshooting) cloud structure, the algorithm is not able to retrieve meaningful information, probably due to the very low temperatures, not represented in the input data and the vanishing sensitivity of the 3.7 µm reflectances to Re

The cloud mask from VIRS channels, can be considered a useful input to the screening procedure of PMW based rain algorithm

Conclusions (2/2)


Extra slides


Wavelenghts

0.623 μm 1.610 μm 3.784 μm 10.826 μm 12.028 μm

Frequency

13.8 GHz

Polarization

V & H V & H V V & H V & H

Frequency

10.65 GHz 19.35 GHz 21.3 GHz 37.0 GHz 85.5 GHz

Scan angle ± 45°Scan angle ± 17°Scan angle ± 65°

TMITMI PRPR VIRSVIRS

Instruments onboard TRMM

Elsa Cattani, Francesca Torricella, and Vincenzo Levizzani ISAC CNR (Italy)

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