Condensational growth of cloud droplets, COST 722 Condensational growth of cloud droplets, with reference to warm cumulus clouds and the impact on formation.

Slides:



Advertisements
Similar presentations
Moisture, Clouds, and Precipitation
Advertisements

Water in the Atmosphere
Clouds and cloud microphysics Wojciech W. Grabowski National Center for Atmospheric Research, Boulder, Colorado, USA (on collaborative leave at CNRM, Toulouse,
3. Droplet Growth by Condensation
7. Radar Meteorology References Battan (1973) Atlas (1989)
Atmospheric Stability
AOSC 200 Lesson 8.
Vertical Structure of the Atmospheric Boundary Layer in Trade Winds Yumin Moon MPO 551 September 26, 2005.
Simulation of Cloud Droplets in Parameterized Shallow Cumulus During RICO and ICARTT Knut von Salzen 1, Richard Leaitch 2, Nicole Shantz 3, Jonathan Abbatt.
Moist Processes ENVI1400: Lecture 7. ENVI 1400 : Meteorology and Forecasting2 Water in the Atmosphere Almost all the water in the atmosphere is contained.
Chapter 9 Vertical Motion. (1) Divergence in two and three dimensions. The del “or gradient” operator is a mathematical operation performed on something.
Tephigrams ENVI1400 : Lecture 8.
Atmospheric Analysis Lecture 3.
A Lagrangian approach to droplet condensation in turbulent clouds Rutger IJzermans, Michael W. Reeks School of Mechanical & Systems Engineering Newcastle.
ENVI3410 : Lecture 8 Ken Carslaw
Textbook chapter 2, p chapter 3, p chapter 4, p Stability and Cloud Development.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
AOS 100: Weather and Climate Instructor: Nick Bassill Class TA: Courtney Obergfell.
AOS 100: Weather and Climate Instructor: Nick Bassill Class TA: Courtney Obergfell.
Convective Weather Thunderstorms Lightning Tornadoes… …and more.
Using microwave radiometer and Doppler lidar data to estimate latent heat Chris. Collier 1, Jenny Davis 1, Fay Davies 1 and Guy Pearson 1,2 1. Centre for.
J. L. Brenguier, F. Burnet, and L. Chaumat 4th IMS Turbulence Workshop, London, March 2009 Discussion or, back to the real world!
1 Radar Displays PPI - Plan position Indicator Maps the received signals on polar coordinates in plan view. The antenna scans 360° at fixed elevation angle.
Cloud Microphysics SOEE3410 : Lecture 4 Ken Carslaw Lecture 2 of a series of 5 on clouds and climate Properties and distribution of clouds Cloud microphysics.
Water in the Atmosphere Water vapor in the air Saturation and nucleation of droplets Moist Adiabatic Lapse Rate Conditional Instability Cloud formation.
ON THE RESPONSE OF HAILSTORMS TO ENHANCED CCN CONCENTRATIONS William R. Cotton Department of Atmospheric Science, Colorado State University.
Lapse Rates and Stability of the Atmosphere
Warm Up 3/14 Which gas is most important for understanding atmospheric processes? a. water vapor c. carbon dioxide b. oxygen d. ozone What is true.
Water’s Changes of State 15 Water in the Atmosphere  Precipitation is any form of water that falls from a cloud.  When it comes to understanding atmospheric.
Unit 4 – Atmospheric Processes. Necessary Atmospheric Conditions 1. Water vapour must be available in the lower atmosphere to feed clouds and precipitation.
GEF2200 Stordal - based on Durkee 10/11/2015 Relative sizes of cloud droplets and raindrops; r is the radius in micrometers, n the number per liter of.
Today’s lecture objectives: –Nucleation of Water Vapor Condensation (W&H 4.2) What besides water vapor do we need to make a cloud? Aren’t all clouds alike?
Cumulus Clouds. What goes on inside a cumulus cloud?
Characterizing CCN Spectra to Investigate the Warm Rain Process by Subhashree Mishra.
4. Initiation of Raindrops by Collision and Coalescence
Chapter 8: Precipitation ATS 572. “Precipitation” Can be: 1.Rain 2.Snow 3.Hail 4.Etc. However, it MUST reach the ground. –Otherwise, it is called “virga”—hydrometeors.
Water in the Atmosphere
Dual-Aircraft Investigation of the inner Core of Hurricane Norbert. Part Ⅲ : Water Budget Gamache, J. F., R. A. Houze, Jr., and F. D. Marks, Jr., 1993:
April Hansen et al. [1997] proposed that absorbing aerosol may reduce cloudiness by modifying the heating rate profiles of the atmosphere. Absorbing.
Moist Processes SOEE1400: Lecture 11. SOEE1400 : Meteorology and Forecasting2 Water in the Atmosphere Almost all the water in the atmosphere is contained.
Cloud Microphysics Liz Page NWS/COMET Hydromet February 2000.
mixing proceeds rapidly If the mixing proceeds rapidly (T_mix is small), the droplet size distribution is shifted toward smaller diameters. then the fields.
Towards a Characterization of Arctic Mixed-Phase Clouds Matthew D. Shupe a, Pavlos Kollias b, Ed Luke b a Cooperative Institute for Research in Environmental.
High-Resolution Simulation of Hurricane Bonnie (1998). Part II: Water Budget SCOTT A. BRAUN J. Atmos. Sci., 63,
Modeling of warm-rain microphysics and dynamics-microphysics interactions with EULAG-based Large Eddy Simulation model W. W. Grabowski 1, A. A. Wyszogrodzki.
Simulation of boundary layer clouds with double-moment microphysics and microphysics-oriented subgrid-scale modeling Dorota Jarecka 1, W. W. Grabowski.
Modelling and observations of droplet growth in clouds A Coals 1, A M Blyth 1, J-L Brenguier 2, A M Gadian 1 and W W Grabowski 3 Understanding the detailed.
Numerical Simulation and Prediction of Supercell Tornadoes Ming Xue School of Meteorology and Center for Analysis and Prediction of Storms University of.
Stratiform Precipitation Fred Carr COMAP NWP Symposium Monday, 13 December 1999.
Horizontal Variability In Microphysical Properties of Mixed-Phase Arctic Clouds David Brown, Michael Poellot – University of North Dakota Clouds are strong.
Lecture 4 Precipitation (1)
Warm Rain Formation by Ultragiant Particles & Cumulus Entrainment Sonia Lasher-Trapp Purdue University In collaboration with Alan Blyth, Univ. of Leeds.
MODELING OF SUBGRID-SCALE MIXING IN LARGE-EDDY SIMULATION OF SHALLOW CONVECTION Dorota Jarecka 1 Wojciech W. Grabowski 2 Hanna Pawlowska 1 Sylwester Arabas.
Towards parameterization of cloud drop size distribution for large scale models Wei-Chun Hsieh Athanasios Nenes Image source: NCAR.
Kinematic, Microphysical, and Precipitation Characteristics of MCSs in TRMM-LBA Robert Cifelli, Walter Petersen, Lawrence Carey, and Steven A. Rutledge.
FOG. Fog is a cloud (usually stratus) that is in contact with the ground. –Relatively stable air ie. Shallow lapse rate needed –Temperature to dew point.
Background – Building their Case “continental” – polluted, aerosol laden “maritime” – clean, pristine Polluted concentrations are 1-2 orders of magnitude.
Cumulus Clouds. Instabilities Resulting in Vertical Overturning 1.Thermal Instability (Assuming uniform vertical pressure gradient) a) Static (Parcel.
High-Resolution Polarimetric Radar Observation of Snow- Generating Cells Karly Reimel May 10, 2016.
Cloud Formation  Ten Basic Types of Clouds (Genera): l High: Ci, Cs, Cc l Middle: As, Ac l Low: St, Ns, Sc l Clouds of Great Vertical Extent: Cu, Cb 
Impact of Cloud Microphysics on the Development of Trailing Stratiform Precipitation in a Simulated Squall Line: Comparison of One- and Two-Moment Schemes.
Microphysical-dynamical interactions in an idealized tropical cyclone simulation Stephen R. Herbener and William R. Cotton Colorado State University, Fort.
Clouds (Condensed PPT)
Reflections on Radar Observations of Mesoscale Precipitation
By SANDRA E. YUTER and ROBERT A. HOUZE JR
Seamless turbulence parametrization across model resolutions
Impact of the vertical resolution on Climate Simulation using CESM
Group interests RICO data required
Review of Roesenfeld et al
Group interests RICO data in support of studies
Presentation transcript:

Condensational growth of cloud droplets, COST 722 Condensational growth of cloud droplets, with reference to warm cumulus clouds and the impact on formation of precipitation sized drops. Alan Gadian, Alan Blyth, Jean-Louis Brenguier, Alison Coals, Wojtek Grabowski, John Latham + others 1 Title

Condensational growth of cloud droplets, COST To examine the growth of droplets in the condensation phase. Under what circumstances does the the droplet spectra become large enough for the coalescence processes to become significant and to start precipitation. Although primarily a study of the condensation processes in cumulus clouds, the immediate extension is to processes in stable Fog clouds. Contents: Numerical Model. Details of Observations … two case studies. Discussion of droplet profiles. Implications for entrainment and mixing in warm clouds and fog. Aims

Condensational growth of cloud droplets, COST In warm clouds, droplet spectra is broader than predicted Bi-modal / multi-modal spectra are observed in cumulus clouds. Limited data from fog suggests that this is not observed. Adiabatic parcel calculations, only in special cases, replicates the multi-modality in warm cumulus clouds. Size distributions need to be represented / predicted accurately. Inadequate representations of cloud droplet size distributions have a number of consequences … coalescence rates rates are sensitive to droplet sizes. This has crucial importance for precipitation development. Brenguier et al developed a simplified approach for the condensational growth equation. Model and observational data are compared: role of homogeneous and in homogeneous processes examined. Background

Condensational growth of cloud droplets, COST 722 4

5 DRY AIR Activation and condensation Dry Air Saturated Air,  = 1 Air rises, cool and enough vapour exists to produce a saturated volume DRY AIR Activation and partial condensation Partially saturated air,  < 1 DRY AIR Activation and condensation Saturated Air,  = 1 Evaporation Partially saturated air,  < 1 Dry Air  = 0 Partial condensation Further condensation occurs in the “wet” part of the cell. No further condensation Random no. used to determine option

Condensational growth of cloud droplets, COST SCMS study The Small Cumulus Microphysics Study was conducted in Florida, near Cape Canaveral, during July and August The objective of the study was to examine the initiation of warm rain in cumulus clouds. Data was from the NCAR CP-2 dual-wavelength radar, the NCAR C-130, the Meteo-France Merlin, and the Wyoming King Air. NCAR C-130 data is displayed here. The cumulus clouds examined during SCMS typically had cloud bases with 950 mb (about 500 m above mean sea level, or MSL) and temperature23C. Here model clouds on two days with maximum observed concentration of cloud drops of about 800 and 500 cm -3 on the 24th July and 10th August. The size distributions observed in clouds on the 10th August was rarely bimodal, which is unusual for the SCMS clouds. This is investigated. Radar, aircraft and visual observations of the Florida small cumulus clouds suggest that the upper parts of the clouds contained single thermals of about 1 km in size, when they initially grew, to about 4km. The initial clouds that ascended to about 4 km usually collapsed and decayed.

Condensational growth of cloud droplets, COST The cloud base temperature and pressure on this day were approximately 23C and 940 mb respectively, corresponding to an altitude of about 700m. Radar scans indicated that the cumulus clouds ascended to about 4 km: this was typical of the cumulus clouds that developed in this area. A temperature and dew-point sounding made 22 km SW of the radar starting at 1838 UTC indicated that the atmosphere was conditionally unstable above a relatively well-mixed boundary layer -- typical of the Florida environment. The wind near the surface was from the west, the direction of mainland Florida. This case is from near the end of the first period of the project where the wind was generally from the west and the concentration of clouds droplets was higher. Data collected by the Particle Measuring Systems FSSP-100 during aircraft penetrations of the cloud showed a maximum cloud droplet concentration of 800 cm -3. July 24 background meteorology

Condensational growth of cloud droplets, COST Time series of data gathered by aircraft. Top panel: 25 Hz vertical wind speed superimposed with wind vectors. Bottom three panels:10 Hz values of mean diameter, total concentration of cloud drops (N), and liquid water content (L) respectively, derived from the FSSP. 10 Hz drop size distributions measured by the FSSP. x-axis: diameter ranging from 0-50  m. y-axis: N(d), ranging from cm -3. July 24 aircraft penetration through the cloud

Condensational growth of cloud droplets, COST Vertical slice through simulated cloud, showing cloud droplet spectra at each grid point (resolution 95 m). N 0 =1000 cm -3., aircraft altitude 2.7km July 24 droplet spectra, vertical velocity and qc

Condensational growth of cloud droplets, COST Vertical cross-sections of simulated cloud water mixing ratio at (top left) 46, (top right) 48, (bottom left) 51, and (bottom right) 53 minutes. July 24 model qc cloud values

Condensational growth of cloud droplets, COST August 10 background meteorology Five vertical scans through the cloud. 14:56, 14:58:15, 15:00:30, 15:02:27, 15:06:39. Horizontal and vertical scales are 1 and 2 km. The cloud base temperature and pressure on this day was approximately 24C and 965 mb, respectively, corresponding to an altitude of about 550 m MSL. Individual clouds reached a height of 5 km. The particular cloud used in this study developed over 11 minutes, reaching a height of about 5 km before collapsing and dissipating. The main radar echo developed to 30 dBZ near cloud top as the cloud was growing. The region of high reflectivity descended as the cloud dissipated. The temperature and dew-point sounding taken at the same location as for the 24 July case at 1408 UTC (1008 local time) indicated that there was conditionally unstable atmosphere above a well-mixed boundary layer. The low-level winds were from along the shoreline. The maximum cloud droplet concentration measured by the PMS FSSP-100 for this day was 530 cm -3, which is consistent with there being a continental component to the CCN distribution.

Condensational growth of cloud droplets, COST August 10 model qc cloud values Vertical cross-sections of simulated cloud water mixing ratio at (top left) 65, (top right) 68, (bottom left) 71, and (bottom right) 73 minutes.

Condensational growth of cloud droplets, COST Time series of data gathered by aircraft. Top panel: 25 Hz vertical wind speed superimposed with wind vectors. Bottom three panels: 10 Hz values of mean diameter, total concentration of cloud drops (N), and liquid water content (L) respectively, derived from the FSSP. 10 Hz drop size distributions measured by the FSSP. x-axis: diameter ranging from 0-50  m. y-axis: N(d), ranging from cm -3. August 10 aircraft penetration through the cloud Simulated drop size distributions (N 0 =500 cm -3) through cloud along flight path (altitude 2.4 km). Each plot represents a model grid point (95 m), and corresponds to approximately 1 second of flight time, from left to right. x-axis: diameter ranging from 0-40  m. y-axis: normalized distribution

Condensational growth of cloud droplets, COST August 10 droplet spectra, vertical velocity and qc Vertical slice through simulated cloud, showing cloud droplet spectra at each grid point (resolution 95 m). N 0 =500 cm -3

Condensational growth of cloud droplets, COST th July case (higher droplet concentration): model predicts the number of drops larger than 25 microns, and shows that the size of the largest drop increases with height in the diluted updraught at x= -0.6 km updraught is the centre part of the thermal circulation and contains a mixture of cloud base air and environmental air. Table 1 shows values of L/L ad ~ 0.6 and w ~ 9 m s -1 ; ideal conditions for enhanced growth due to entrainment and mixing (Baker et al, 1980). observations suggest that although the number concentration is likely constant in the updraught due to re-activation of CCN, the larger drops compete more effectively for the water vapour, and growth of these drops is favoured (Baker et al, 1980). 10 th August case (medium droplet concentration): observed and modelled spectra both indicate less bimodality and are narrower than the 24 th July case simulation spectra indicate the effects of the turbulent mixing and entrainment processes at the top, sides, and around the “holes” – in this case only observable near the top of the cloud. evidence of broadening of spectra in updraught after entrainment occurs. Sensitivity studies show bimodality decreases with increasing N 0 Summary - 1

Condensational growth of cloud droplets, COST A high-resolution, 3D cloud model was to examine the evolution of the droplet size distributions during the development stages of a cumulus cloud. The air motions within the model cloud were initially consistent with a simple thermal circulation with an updraught over most of the cloud, divergence near cloud top, downdraughts at the cloud edges, and inflow at the rear of the thermal into the updraught. Turbulence destroyed this simple pattern and the symmetry of the flow, although the general pattern was always present. The DSD’s can be explained in the context of this thermal model. For example, the DSDs were narrow in the strong updraughts while the peak of the DSDs shifted to smaller sizes at the sides of the cloud and at cloud top where the cloud has been diluted due to entrainment. Also, the size distributions were bimodal in the updraught near the rear of the thermal where the liquid water content was reduced. It is likely that the peak at smaller sizes is due to activation on CCN that are either entrained or that result from evaporation of cloud drops. Ascending regions of cloud with reduced liquid water content are the right ingredients for the enhanced growth of large drops proposed by Baker et al.(1980) and Cooper (1987). The model results showed that larger drops were produced at the top of diluted updraughts in both cases. The DSD’s show bimodality and growth with turbulence mixing and entrainment. Modelling Fog requires that these processes be accounted for. Summary - 2

Condensational growth of cloud droplets, COST Observations were only made at a single level ---- limited comparison only but encouraging that the model results are similar to the observed ones in terms of the breadth of the DSDs, relative locations of bimodal DSDs, and behaviour of the DSDs at cloud edge. Sensitivity studies, for different concentrations of CCN, No. Increasing No in the 24 July and the 10 August cases, produces fewer bimodal size distributions because the the main peak of the distribution was at a smaller size. However, no other significant differences were found. In partially-saturated model regions where evaporation and condensation occur, the trajectory of the air parcel into a ``cloudy'' or ``dry'’ sector is determined by a random number comparison, depending on the value of  for the cloudy grid. Future development e.g. incorporation of a turbulence-related weighting factor, could better describe physical processes of entrainment. Role of entrainment is important in broadening. Turbulent mixing is very much a three-dimensional process. The vortices tend to exist for longer in the 3D simulations. Despite the simplicity, the model results compared well with the observations. Summary - 3