EURAINSAT European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale

1. EURAINSAT European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale V. Levizzani Consiglio Nazionale delle Ricerche Istituto di Scienze dell’Atmosfera e del Clima Bologna and all EURAINSAT Scientists

2. Increasing demand for local and global products for: Products for developing countries (e.g. Africa) Monitoring of remote areas Applications to short range forecasting and nowcasting Agriculture (crop control, irrigation,…) Assimilation into NWP models (eg. latent heat nudging, physical initialization,…) Weather modification … Climate and Global Change Large underestimation of the role of precipitation processes

3. In particular, for meteorology: Instantaneous rapid update estimates for hydrology: Disaster management (eg. Flash flood); Use in coupled LAM + hydrological models that include runoff. Identification of orographic enhancement and monitoring of extreme events. Correct determination of precipitation not only in case of deep convection, but also for frontal and stratiform rainfall in general.

4. What is EURAINSAT? A shared-cost project (contract EVG1-2000-00030) co-funded by the Research DG of the European Commission within the RTD activities of a generic nature of the Environment and Sustainable Development sub-programme (5th Framework Programme). Funded over a 3-year period starting January 1st, 2001. What is the main purpose? Develop new satellite rainfall estimation methods at the geostationary scale for an operational use in short and very short range weather monitoring and forecasting. • Who are the key target users? • The project is very much application-oriented and natural users are to be found among: • National and regional met services, • Basin authorities, • International agencies (WMO, FAO, …), • National and international space agencies, • National agencies for civil and environmental protection, • Institutions for the protection against hydrogeological risks, • Air traffic control centers, • Research institutions, • Industry, agriculture, …

5. EURAINSAT - European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale The Consortium: ITALY V. Levizzani, A. Buzzi, F. Tampieri CNR – Istituto di Scienze dell’Atmosfera e dell’Oceano, Bologna A. Mugnai CNR – Istituto di Fisica dell’Atmosfera, Roma F. Meneguzzo Laboratorio per la Meteorologia e le Modellistica Ambientale (LAMMA), Firenze F. S. Marzano Univ. dell’Aquila, Dip. di Ingegneria Elettrica, Monteluco di Roio F. Prodi Univ. di Ferrara, Dip. di Fisica, Ferrara ISRAEL D. Rosenfeld, A. Khain Hebrew University of Jerusalem, Institute of Earth Sciences, Jerusalem GERMANY M. Kästner German Aerospace Centre (DLR), Oberpfaffenhofen UNITED KINGDOM C. Kidd Univ. of Birmingham, School of Geography and Environmental Sci., Edgbaston, Birmingham

6. EURAINSAT - European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale EXTERNAL STEERING AND COOPERATION: European Centre for Medium-Range Weather Forecasts, Reading, UK, P. Bauer European Organisation for the Exploitation of Meteorological Satellites, Darmstadt, Germany, J. Schmetz European Space Agency, Nordwijk, The Netherlands, J. P. V. Poiares Baptista NASA, Goddard Space Flight Center, Greenbelt, Maryland, E. A. Smith Naval Research Laboratory, Monterey, California, J. F. Turk NOAA-NESDIS, Office of Research and Applications, Silver Springs, Maryland, J. F. W. Purdom Raytheon ITSS, Distributed Active Archive Center, NASA-GSFC, Greenbelt, Maryland, G. A. Vicente World Meteorological Organization, Geneva, Switzerland, D. E. Hinsman

7. Research activities: • Precipitating system structure • Quantitative rainfall estimations Operational Meteorology • Assimilation into NWP Local Area Models

8. From simple VIS-IR MW estimates • To combined estimates • that are more • Microphysically correct, • Linked to operational • requirements, • To be assimilated into • NWP models • Through: • Multispectral cloud microphysical • characterization • Cloud modeling • Rapid update cycles

9. METEOSAT Second Generation (MSG)

10. Meteosat MSG Visible spectrum 0.5-0.9 m 0.75 m broadband 0.56-0.71 0.74-0.88 1.50-1.78 Water vapor spectrum 6.4 5.35-7.15 6.85-7.85 Infrared windows 11.5 3.48-4.36 8.30-9.10 9.80-11.80 11.0-13.0 Pseudo-sounding 9.38-9.44 12.4-14.4 Sampling distance 2.25-km (visible) 4.5-km (others) 1-km (hi-res visible) 3-km (others) On-Earth pixel resolution 2.25-km (visible) 5-km (others) 1.4-km (hi-res visible) 4.8-km (others) Update cycle 30-minutes 15-minutes MSG Spinning Enhanced Visible and Infrared Imager (SEVIRI)

11. Wavelength (m) CloudSat Using MODIS prior to the MSG Launch Transmittance AMSR-E SSM/I AMSU-A,B

12. Key target areas and experiments

13. Frontal system and heavy rain over the Lago Maggiore region (NW Italy and Switzerland) Frontal passage and cyclogenesis over Northern Italy Persistent lifting of stable air during a frontal passage over the Alps IOP 2 18-21 Sept. 1999 Cold frontal passage over the Alps IOP 5 2-5 Oct. 1999 IOP 8 20-22 Oct. 1999 IOP 15 5-10 Nov. 1999

14. UK and Northern Europe Light and/or Sustained rain 30 Oct. 2000 UK and Europe sustained rain from several subsequent storms 28 June 2001 Insignificant rain over the UK. Interesting case to test the sensitivity of rain algorithms to very light rain.

15. Climatological areas Europe and Africa

16. Areas for cloud microphysics

17. Strategies Use of new active and passive sensors: MW instruments VIS/IR/NIR channels Precipitation radars Development of hybrid IR/MW rainfall algorithms in rapid update Assimilation of rainfall fields in NWP models

18. Rosenfeld, D., 1999: TRMM observed first direct evidence of smoke from forest fires inhibiting rainfall. Geophys. Res. Lett., 26 (20), 3105-3108. Impactof smoke particles on cloudmicrostructureand precipitation… TRMM VIRS image of fires, smoke and clouds over Kalimantan, Indonesia, from 1 March 1998, 02:50 UTC. The color is composed of: red for visible reflectance, green for 3.7 m reflectance (approximating re), and blue for the inverse of 10.8 m brightness temperature. The northwest coast of the island is denoted by the yellow line. The small orange areas on the upper right (east) corner are hot spots indicating the fires. The smoke, streaming from the hot spots south-westward, is indicated by the fuzzy purple color of the background. The smoke-free background is blue. This color scheme shows clouds with small droplets (re<10 m) as white, becoming yellow at the supercooled temperatures. Clouds with larger droplets (re>15 m) are colored pink, and cold ice clouds appear red. The black hatching marks the areas in which the TRMM radar detected precipitation. Vertical cross section along the line AB in the above figure, where the left end is point A and the right end correspond to point B. The gray area is the clouds, as measured by their top temperature. The colors represent the precipitation reflectivity, in dBZ, as measured by the TRMM radar. The white line is the brightness temperature of the TRMM Microwave Imager 85 GHz vertical polarization, plotted at the altitude of that temperature.

20. Maximum Reflectivity & Lightning Events Stratiform Convective Normalized LIS event vs maximum reflectivity Dietrich et al., 2001

21. Strategies Use of new active and passive sensors: MW instruments VIS/IR/NIR channels Precipitation radars Development of hybrid IR/MW rainfall algorithms in rapid update Assimilation of rainfall fields in NWP models

22. Elements of a Global Precipitation Analysis microphysical information dynamical information Infrequent microwave-based rainfall estimates Global or regional-scale model forecast Orographic adjustment, cloud growth/decay adjustment Rapidly updated IR-based observations space-time information

23. An Information Transfer Perspective In essence, the procedure is an information-transfer. How much and for how long is microphysical information from past microwave overpasses maintained? What are the best techniques to forward-propagate past information (microwave observations, multispectral IR observations)? t-2 t-1 t0 t+1 etc. Shaded box represents the previous-time “window” prior to t0 Denotes equally-spaced geostationary-based IR observation Denotes non-routine, non-equally spaced microwave-based observation

24. 24-hour accumulations from geostationary-based technique 2000/04/27 1200 UTC 24-hour accumulations from merged microwave sensors (F-11,13,14,15; TRMM)

25. 21 June 2001. Daily rainfall totals (mm) from a combined microwave-infrared rainfall estimation technique. Infrared cloud top brightness temperatures are calibrated using passive microwave estimates updated on a daily basis. Data fusion and artificial neural networks are also being evaluated. Chris Kidd, Univ. of Birmingham, UK.

26. 24-hour accumulations from merged microwave sensors (F-11,13,14,15; TRMM) 2000/05/16 0300 UTC Limited overpasses over Italian coast Some possible false identification in Alps 24-hour accumulations from geostationary-based technique 2000/05/16 0300 UTC Local flood event in southwest coast is captured

27. Strengths/Weaknesses Ancillary Data: • From simple rainfall estimate using: • VISIR • (GOES/MSG/MODIS) • Microwave • (SSM/I, TRMM, AMSU, AMSR) Orographic adjustment • Through: • Multispectral cloud microphysical • characterization • Cloud modeling • Mesoscale Forecasts • Rapid update cycles • Space/Time information • To combined estimates • that are: • Microphysically correct, • Linked to operational • requirements, • To be assimilated into • NWP models 24-hr Accumulations COAMPS Surface Winds Topography Adjusted • Strengths: • Convective-based rain systems • Typically heaviest rain locations • Slower moving systems, e.g., tropical • cyclones • Accumulations on a daily scale or longer • Adaptation to daily changes • Correlations near 0.6 with land gauge • data • Well-suited for insertion into numerical • models • Soil moisture analysis (land data • assimilation) • Weaknesses: • Defining the rain/no-rain threshold • Areas of light (< 0.5 mm/hr) precipitation • tend to be too widespread • Fast moving mid-latitude systems • Insufficient observations of precipitation • development from necessary spectral regions • Movement over areas of complex topography • Proper accounting for orographic precipitation • and rain shadowing effects

28. Strategies Use of new active and passive sensors: MW instruments VIS/IR/NIR channels Precipitation radars Development of hybrid IR/MW rainfall algorithms in rapid update Assimilation of rainfall fields in NWP models

29. If Rsat > Rfor increase q over saturation qnew(z) = qsatur(z) + c(t,z) (Rsat-Rfor) if Rsat < Rfor decrease q qnew(z) = qsatur(z) + d(t,z) (Rsat-Rfor)  {q(z) – qref(z)} where qref(z) is the reference humidity profile and c and d are nudging coefficients.

30. First results

31. Calabria Flood 8 Sept 2000 1030 UTC MODIS ch02 0.86 m

32. Calabria Flood 9 Sept 2000 0935 UTC MODIS ch02 0.86 m

33. Features of the FSU Superensemble Forecast System: • Real-time assimilation of SSMI, TRMM 2A12, and blended microwave/IR rain rate algorithms and techniques via a physical initialization (ie, reverse initialization) • Forecast uses multi-analysis forecasts (12 different models) and statistics from a training phase to produce superensemble forecasts of precipitation • Day 2 and 3 forecasts show improved skill in precipitation forecast compared to operational models that do not employ physical initialization • This forecast technique is promising for the prediction and guidance of extreme rain events in flood prone areas T.N.Krishnamurti, FSU J.F.Turk, NRL

34. 12 UTC September 8, 2000: Observed and 1,2,3-day forecasted average precipitation

35. Orographic Conditions: California frontal passage January 11, 2001 0000 UTC

36. METEOSAT-7 IR image SSM/I 85-GHz Brightness Temperature 24-h rainfall accumulation (mm) 20-GHz Path Attenuation at 0600 UTC 6 October, 1998 F. S. Marzano – Univ. of L’Aquila

37. Want to know about the future? We will not only think in terms of: Using single satellite platforms Adopting a synergy of satellites conceived for different uses What’s boiling in the pot?

39. International Precipitation Working Group (IPWG) • co-sponsored by • Coordination Group for Meteorological Satellites (CGMS) • and • World Meteorological Organization (WMO) • Co-chairs • Arnold Gruber, NOAA-NESDIS • Vincenzo Levizzani, ISAC-CNR • The IPWG is established to foster the: • Development of better measurements, and improvement of their utilization; • Improvement of scientific understanding; • Development of international partnerships.

40. 1st Meeting in Ft. Collins, CO CSU, 20-22 June, 2001

41. International Precipitation Working Group (IPWG) • The objectives of the IPWG are: • to promote standard operational procedures and common software for deriving precipitation measurements from satellites; • to establish standards for validation and independent verification of precipitation measurements derived from satellite data; including: • reference standards for the validation of precipitation for weather, hydrometeorological • and climate applications; • standard analysis techniques that quantify the uncertainty of ground-based measurements • over relevant time and space scales needed by satellite products; • to deviseand implement regular procedures for the exchange of data on inter-comparisons of operational precipitation measurements from satellites; • to stimulate increased international scientific research and development in this field and to establish routine means of exchanging scientific results and verification results; • to make recommendations to national and international agencies regarding the utilization of current and future satellite instruments on both polar and geostationary platforms; and • to encourage regular education and training activities with the goal of improving global utilization of remote sensing data for precipitation measurements.

42. http://www.isao.bo.cnr.it/~eurainsat