PPrroocceeeeddiinnggss 1st Workshop

Madrid, Spain23-27 September 2002

MULTISPECTRAL OBSERVATIONS OF CLOUD TOP AS A POWERFUL TOOL FOR RAINFALL ESTIMATIONS

Vincenzo Levizzani1, Daniel Rosenfeld2, Elsa Cattani1, Samantha Melani1, Francesca Torricella1, and Maria João Costa1,3

Abstract

The sensors in the infrared, near infrared and visible of polar orbiting and geostationary satellites have entered a new era where the number of channels and the data availability is greater than ever. The use of data from these channels is not anymore confined to the attribution of rainfall levels to cloud top brightness temperature fields as an indirect way of estimating precipitation at the ground. Channels at 1.6, 2.1 and 3.7 µm provide insights into cloud and precipitation formation mechanisms that are uniquely observed at these wavelengths. A perspective on the most recent advances in satellite cloud multispectral studies will be presented with special emphasis on Cloud characterization (effective radius, optical thickness, supercooled water,…), Identification of cloud type (maritime-continental, convective-stratiform), and Use of multispectral data together with outputs of cloud models with explicit microphysics for

the improvement of MW retrievals.

1. Introduction Channels at 1.6, 2.1 and 3.7 µm spanning the visible (VIS), near infrared (NIR) and infrared (IR), among others, are now part of the common payload of meteorological and environmental satellites: e.g. the Advanced Very High Resolution Radiometer (AVHRR) on board the National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites, the MODerate resolution Imaging Spectroradiometer (MODIS) on board the Earth Observing System (EOS) satellites. Some of the channels are included in the radiometers of geostationary satellites, the Geostationary Operational Environmental Satellite (GOES) and Meteosat Second Generation’s (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) (Schmetz et al., 2002). These wavebands can be used for the following major cloud applications/retrievals: • Cloud effective radius, re (Nakajima and King, 1990; Nakajima and Nakajima, 1995; Baum et

al., 2000). • Cloud phase (ice or water) (Hutchison, 1999; Inoue, 2000; Inoue and Aonashi, 2000; Platnick

et al., 2003). • Discrimination between clouds and ground ice/snow (Hutchison, 1999; Inoue, 2000). • Identification of ice and snow on the ground, and sea ice (Hutchison et al., 1997).

1 CNR-Institute of Atmospheric Sciences and Climate, via Gobetti 101, I-40129, Bologna, Italy 2 Institute of Earth Sciences, Hebrew University of Jerusalem, Jerusalem 91904, Israel 3 Department of Physics, University of Évora, Évora, Portugal

• The 1.6 µm spectral band, together with the channels in the VIS spectral range, lends itself better for the discrimination between small aerosols and desert dust and for aerosol property retrieval in general: e.g. the applications of MODIS (King et al., 1992, 2003).

2. Necessity of cloud multispectral information The retrieval of cloud properties is a major application area of the satellite multispectral analysis techniques. This consideration, which seems quite trivial in nature, is in reality responding to a real necessity of recovering cloud physics content into rainfall estimation techniques. It is certainly not fair to say that the estimation methods do not take into account the microphysical aspects of precipitation formation into their schemes, but it is true that we tend to forget most critical aspects of cloud formation in favor of more technical issues such as sensor performances, resolution, and other important issues. The idea of exploiting the VIS, NIR and IR channels tries to recover the microphysical insights that would otherwise be undermined in most precipitation-oriented satellite observations. Cloud effective radius re and thermodynamic phase, which are mainly inferred using the 3.7 µm channel, are used for a great number of applications. Most of these applications are just emerging. The moment, however, is very critical since cloud microphysics as derived from VIS, NIR and IR channels will enter the chain of operational meteorology soon. With a great deal of simplification, the upcoming applications can be summarized as: • Retrieval of liquid water path (e.g. Han et al., 1999). • Retrieval of supercooled water in clouds (Rosenfeld and Woodley, 2000). • Retrieval of precipitation forming processes in the clouds (Rosenfeld and Lensky, 1998) also

relevant for global change issues (e.g. Rosenfeld and Woodley, 2003). • Improved rainfall measurements from space and ground based radars (Rosenfeld and Ulbrich,

2003). • Improved satellite rainfall measurements using VIS/IR multispectral methods (e.g. Ba and

Gruber, 2001; Levizzani et al., 2001; Sorooshian et al., 2000; Vicente et al., 1998) and microwave (MW) algorithms, especially rapid update hybrid techniques (e.g. Todd et al., 2001; Turk et al, 2000).

• Identification of cloud types, convective or stratiform, by their microstructure (Rosenfeld and Lensky, 1998) and identification of the early stages in the development of severe storms.

• Identification of air pollution by the impact on cloud microstructure (Rosenfeld 1999, 2000). • Climate impacts of aerosols by their interactions with clouds (e.g. Nakajima et al., 2001; Breon

et al., 2002). Theoretically, all channels at 1.6, 2.1 and 3.7 µm are equally suitable for retrieving cloud particle re and thermodynamic phase, when clouds are vertically homogeneous and surface properties are well known (Baum and Spinhirne, 2000; Rolland et al., 2000). However, these two assumptions occur rarely for the same cloud. A critical essay on the correct use of 1.6 and 3.7 µm channels is provided by Rosenfeld et al. (2003). 3. Are multispectral observations representative of cloud microphysics? The question is trivial at a first sight, but it becomes relevant when interpretation of retrievals and their application to rainfall estimation algorithms. It is common knowledge that VIS/IR signals respond to cloud top hydrometeors. In reality, however, different channels respond differently to increasing depths underneath the cloud top. Fig. 1 shows how the 1.6 and 3.7 µm channels of the Visible and Infrared Scanner (VIRS) respond differently when observing the same cloud scene. Reflectances in both channels were used to

retrieve cloud top temperature (T) profiles as a function of re (Rosenfeld and Lensky, 1998). While the T - re graphs at 3.7 µm look regular and show a behavior typical of oceanic precipitating clouds, those at 1.6 µm are much more noisy and re values unreasonably off-scale are produced. This fact suggests that the 1.6 µm channel “sees” something different from what the 3.7 µm sees. The impression is confirmed by Fig. 2-4 where three cloud scenarios are presented where the “penetration” of the 1.6, 2.1 and 3.7 µm channels are reported. In other words, the figures show how in depth in the cloud system the signal of the three channels comes from. This has obvious consequences on the capabilities of the channels to be responsive to cloud top or near-cloud-top microphysics (Rosenfeld et al., 2003). Note that, if the cloud is semi-transparent, additional contributions to the satellite-observed radiances may come from the underlying surfaces such as other cloud decks or the ground. In summary, there is no such a thing as a channel that outperforms the others. There are simply channels that respond to different cloud characteristics and therefore need to be all used in synergy (Chang and Li, 2002). It is, however, undoubted that the 3.7 µm channel is more suitable for re retrievals and thus it proves a better tool for cloud top microphysics in

connection to rainfall retrievals.

Figure 1. 8 Nov. 1998. Multispectral image fromTRMM VIRS over the Kwajalein Atoll withsuperimposed PR-derived rain areas (stippledpixels). The graphs report cloud top temperature vseffective radius. While the 3.7 µm product (top)correctly identifies the cloud top structure anddelimits the various microphysical zones, the 1.6µm channel response (bottom) suffers fromcontamination from the lower levels in the cloud.Numbers on the graphs refer to the numberedboxes in the image.

4. The future: use of multispectral imagery for improving rainfall estimations The current general perception is that the microphysical content of satellite measurements is largely underused for the construction of rainfall estimation algorithms. Perhaps the most direct way of using cloud microphysics is that of statistical-physical MW-based codes that make use of cloud-radiation databases (e.g. Tassa et al., 2003). Microphysical profiles from mesoscale and cloud model runs for different cloud systems give reason of MW signals over hurricanes, convection, fronts and midlatitude precipitation systems. Presently, this is the most physical way to make sure that rainfall algorithms incorporate precipitation formation processes. However, MW algorithms need substantial improvements over land and coastal areas and in the case of shallow/inconspicuous rainfall regimes. Some algorithms have started to take into account possible contributions from multispectral measurements. Ba and Gruber (2001) use a multispectral approach to optimize the identification of raining clouds located at a given altitude estimated from the cloud-top temperature. They combine information from five channels on the GOES satellites: VIS (0.65 µm), NIR (3.9 µm), water vapor (6.7 µm), and IR window channels (11 and 12 µm). The screening of nonraining clouds includes the use of spatial gradient of cloud-top temperature for cirrus clouds and the re of cloud-top particles derived from the measurements at 3.9 µm during daytime. During nighttime, only clouds colder than 230 K are considered for the screening; during daytime, all clouds having a visible

reflectance greater than 0.40 are considered for the screening, and a threshold of re = 15 µm in droplet effective radius is used as a low boundary of raining clouds. Other schemes adopt more or less the same strategy. Ideas are brought forward by Reudenbach and Bendix (2003) in another paper of these proceedings.

Figure 2. Response of the 1.6, 2.1and 3.7 µm MODIS channels forvarious re values at cloud top in thecase of continental clouds overColorado consisting of small drops.Liquid water content is 0.5 g m-3,cloud depth for minimal surfaceeffects at τ0.5 is 30.

Figure 3. Same as in Fig. 2 but formaritime clouds over Hawaiiconsisting of large drops.

Figure 4. Same as in Fig. 2 but forwater clouds at the top and icehydrometeors below.

Advancements along this path will have to include the increasing understanding of the physical content of VIS, NIR and IR observations. The findings of Rosenfeld and Lensky (1998) need to be linked to explicit cloud modeling (e.g. Khain et al., 2001) and improve the capacity of MW measurements to detect precipitation over land, in coastal areas and in conditions of light rain.

Acknowledgments - Funding was provided by EURAINSAT (http://www.isac.cnr.it/~eurainsat/), a shared-cost project (contract EVG1-2000-00030) co-funded by the Research Directorate General of the European Commission within the research and technological development activities of a generic nature of the Environment and Sustainable Development subprogram (5th Framework Programme). One of the authors (DR) acknowledges partial funding from the Israeli Space Agency. The Italian authors wish to thank the Italian Space Agency (ASI) and the National Group for the Prevention from Hydrogeological Disasters (GNDCI). The Italy-Israel bilateral agreement CNR-MOS deserves special thanks for making available travel funds to DR and VL. 5. References Ba, M. B., and A. Gruber, 2001: GOES multispectral rainfall algorithm (GMSRA). J. Appl. Meteor.,

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