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Clouds and precipitation: a weather and climate perspective Vincenzo Levizzani Institute of Atmospheric Sciences and Climate National Research Council.

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Presentation on theme: "Clouds and precipitation: a weather and climate perspective Vincenzo Levizzani Institute of Atmospheric Sciences and Climate National Research Council."— Presentation transcript:

1 Clouds and precipitation: a weather and climate perspective Vincenzo Levizzani Institute of Atmospheric Sciences and Climate National Research Council and University of Bologna, Italy

2 C. Adamo, F. Baordo,A. Buzzi, E. Cattani, S. Dietrich,M. Celano, M. J. Costa, S. Davolio, M. Fantini, P. Malguzzi, A. Maurizi, C. M. Medaglia,S. Pinori,V. Poli, F. Tampieri, F. Torricella, M. G. Villani, ISAC-CNR, Bologna, Italy C. Adamo, F. Baordo, A. Buzzi, E. Cattani, S. Dietrich, M. Celano, M. J. Costa, S. Davolio, M. Fantini, P. Malguzzi, A. Maurizi, C. M. Medaglia, S. Pinori, V. Poli, F. Tampieri, F. Torricella, M. G. Villani, ISAC-CNR, Bologna, Italy A. Antonini, S. Melani, G. Messeri, A. Orlandi, A. Ortolani, M. Pasqui, LaMMA, Firenze, Italy A. Battaglia, D. Capacci, C. Caracciolo, S. Mantovani, S. Natali, F. Porcù, F. Prodi, Dept. of Physics, Univ. of Ferrara, Italy D. Cimini, G. Giuliani, F. S. Marzano, Dept. of Electric Engineering, Univ. of L’Aquila, Italy P. P. Alberoni, ARPA-Servizio Idrometeo, Bologna, Italy A. Khain, R. Lahav, I. Lensky, D. Rosenfeld, Earth Sciences Institute, Hebrew Univ. of Jerusalem, Israel M. Kästner, J. Steinwagner, DLR, Oberpfaffenhofen, Germany C. Kidd, J. Kidd, V. Sanderson, F. J. Tapiador, School of Geography and Environmental Sciences, Univ. of Birmingham, UK A. J. Illingworth, R. Hogan, Dept. of Meteorology, Univ. of Reading, Reading, UK D. Kniveton, R. Layberry, School of Science and Technology, Univ. of Sussex, Falmer, UK P. Bauer, S. Di Michele, ECMWF, Reading, UK J. Schmetz, EUMETSAT, Darmstadt, Germany J. P. V. Poiares Baptista, ESA, Noordwijk, The Netherlands R. E. Carbone, A. Laing, NCAR, Boulder, CO, USA E. A. Smith, G. A. Vicente, NASA, Goddard Space Flight Center, Greenbelt, MD, USA J. F. W. Purdom, CIRA, Colorado State Univ., Ft. Collins, CO, USA G. Panegrossi, G. J. Tripoli, Dept. of Atmospheric and Oceanic Sciences, Univ. Wisconsin, Madison, WI, USA F. J. Turk, Marine Meteorology Division, Naval Research Laboratory, Monterey, CA, USA

3 Cloud structure observations…

4 The classification scheme of convective clouds into microphysical zones according to the shape of the temperature – effective radius relations Note that in extremely continental clouds r e at cloud base is very small, the coalescence zone vanishes, mixed phase zone starts at T<-15 o C, and the glaciation can occur at the most extreme situation at the height of homogeneous freezing temperature of –39 o C. In contrast, maritime clouds start with large r e at their base, crossing the precipitation threshold of 14  m short distance above the base. The deep rainout zone is indicative of fully developed warm rain processes in the maritime clouds. The large droplets freeze at relatively high temperatures, resulting in a shallow mixed phase zone and a glaciation temperature reached near –10 o C Rosenfeld and Lensky, 1998

5 1.Large warm ice 2.Large cold ice 3.Small cold ice 4.Small cold water 5.Large warm water

6 1.Large warm ice 2.Large cold ice 3.Small cold ice 4.Small cold water 5.Large warm water

7 1.Large warm ice 2.Large cold ice 3.Small cold ice 4.Small cold water 5.Large warm water

8 MSG _05W40N_ Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain showers. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow. 2

9 MSG _05W40N_ Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain showers. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow.

10 MSG _05W40N_ Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain showers. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow.

11 MSG _05W40N_ Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain showers. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow.

12 MSG _05W40N_ Multilayer mature cloud. Low cirrus above Low Cu+Sc. Little or no rain. Dark red above yellow-white. 2. Thunderstorms. Orange tint on red. 3. Mature rain cloud, moderate rain. Dark red + magenta. 4. Sc+Cu. no-precip. Yellow-white. 5. Local heavy rain shower. Bright Red. 6. Light warm rain showers. Bright Magenta. 7. High level shield, raining on the east side. Orange riding over red. 8. Mid-level orographic clouds. No rain. Intense yellos. 9. Ciro-cumulus. No rain. Dirty yellow.

13

14 Cloud microphysics: mixed-phase Radar and lidar can distinguish liquid and ice phases Calculations show the presence of supercooled liquid water to be fundamental to for the radiative properties of cloud Yet representation in models is very crude and unevaluated Hogan et al. (QJRMS 2003) Similar frequency of occurrence from Chilbolton lidar and “LITE” lidar on shuttle in months of data

15 Extend evaluation of model clouds to the whole globe Exploit European expertise in modelling and assimilation and in combined radar & lidar algorithms CloudSat & Calipso (NASA 2004) Cloud radar & lidar, separate platforms Part of the “A-train” of satellites MODIS & CERES instruments on AQUA Also Parasol and Aura satellites EarthCARE (ESA/NASDA 2008) Cloud radar & lidar on single platform Also MSI, FTS and BBI instruments Radar sensitivity 10x CloudSat Doppler capability for ice fall speed High spectral-resolution lidar for 

16 Understanding of the behavior of precipitation systems worldwide. An ongoing effort in North-Central- South America, Asia, Australia, Europe and Africa for warm season precipitation

17 …for better cloud modeling

18 Integrity of simulated microphysical structure –Liquid vs. ice –Number concentration –Size distribution –Ice habit Integrity of dynamical simulation –Cloud initiated from hot bubble in a representative sounding –Clouds in context of parent weather system Representativeness of data sets – Location on Earth –Parent weather system, i.e. tropical cyclone, cumulus, PBL MCC, etc –Where in storm, i.e., anvil, new convective tower, old convective tower, over cold, occluded or stationary front? –Depth –Stratiform vs. convective –Column representativeness, ie relationship of view at top to processes below (in radiation stream) Idealized supercell Hurricane Bonnie Idealized Lake effectt Piemonte orographic flooding raqin Genoa flash flood Chicago Frontal snow

19 Habit: Categorization Precise Categories –Planar dendrites –Spatial dendrites –Hexagonal Plates –Capped columns –Needles –….etc Simplified Categories –Cloud drops –Rain drops –Pristine crystals –Aggregated crystals –Low density graupel (Rimed crystals) –High density graupel

20 Size Representation Bulk Microphysics – Assume a statistical distribution of sizes and predict 1-3 parameters of the distribution Explicit Microphysics – Divide precip into size bins and explicitly predict each size bin

21 Genova Flood Simulation (UW-NMS)

22 US EAST COAST SNOW STORM: UW-NMS SIMULATED COLUMNAR LWC/IWC’S (06.00, JAN. 25)

23 CROSS SECTION A : LIQUID/ICE WATER CONTENTS

24 O 2 SOUNDING CHANNELS TB DIFFERENCES

25

26 …and rainfall measurements and assimilation into NWP models

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29 Global rainfall estimation

30 Idealized Experiments  METHOD: Lagged Forecast scheme (Algeria flood, Nov. 2001) Two different simulations from initial condition 12 hours apart: “Control Run”: represents the reference state and provides the target rain rate. “Forecast Run”: represents the real forecast to be improved. Nudging procedure applied for 12 hours to a simulation starting from the same initial condition of the Forecast Run (Nudging Run).

31 Different rainfall patterns over the coast and south of the Balearic Islands Rain band missing in the forecast run Rain band and area of light rainfall around Sardinia

32 Improved! Rain band slightly shifted westward but correct in intensity Rain band in phase but intensity too low (36 mm/12h instead of 67 mm/12h)

33 Results at the end of the nudging stage Hit Rate and False Alarm Rate - 6h precipitation forecast nudging Hit RateFalse Alarm X axis: precipitation thresholds (mm/6h) ( ) = n. points where obs. rain rate > threshold

34 RESULTS after the nudging stage Equitable Threat Score vs simulation time Threshold: 2mm/6hThreshold: 10mm/6h end of nudging

35 Thanks for your patience International Precipitation Working Group (IPWG)


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