Clouds, Precipitation and their Remote Sensing

Transcription

1 Clouds, Precipitation and their Remote Sensing Prof. Susanne Crewell AG Integrated Remote Sensing Institute for Geophysics and Meteorology University of Cologne Susanne Crewell, Kompaktkurs, Jülich September Intergovernmental Panel on Climate Change (IPCC) Nobel price IPCC Fourth Assessment Report (FAR), 2007: "Warming of the climate system is unequivocal", and "Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations". Aerosols, clouds and their interaction with climate is still the most uncertain area of climate change and require multidisciplinary coordinated research efforts. Susanne Crewell, ell l, Kompaktkurs, kurs, Jülich 25 September

2 Why are clouds so complex? Cloud microphysical processes occur on small spatial scales and need to be parametrized in atmospheric models Cloud microphysics is strongly connected to other sub-grid scale processes (turbulence, radiation) Cloud droplets 0.01 mm diameter per cm 3 Drizzle droplets 0.1 mm diameter 1 per cm 3 Condensation nuclei mm diameter 1000 per cm 3 Rain drops ca. 1 mm diameter, 1 drops per liter From hydrometeors Why are clouds so complex? to single clouds to Einzelwolken and cloud fields to the global system 2

3 What are important cloud parameter? Macro-physical Parameter cloud fraction cloud height cloud contours 3D-structure Radiative Quantities extinction coefficient ε [m -1 ] optical thickness τ τ λ = z= z=0 ε λ (z') dz' transmission t = exp(-τ) Micro-physical Parameter number concentration N effective radius r eff liquid water content LWC radar reflectivity Z moments of the droplet spectrum Ice- and mixed phase clouds phase shape density Droplet spectra observations modelling Hawaii orographic Hawaii stratus Passat Australia continental moments of drop spectra cloud liquid water density [kg m -3 ] m(n) = r n N(r)dr 0 0 LWC = 4π 3 ρ r 3 w N(r)dr 3

4 Ice and mixed phase clouds Bergeron-Findeisen While everywhere sufficient cloud condensation nuclei for forming water droplets are available, much fewer ice nuclei exist From small to large particles μm 1.0 μm 10 μm aerosols cloud droplets ice crystals 100 μm 1.0 mm 10 mm rain drops snow turbulence microphysical models ~100 m numerical weather prediction (NWP) models ~10 km climate Susanne models Crewell, ~100 Kompaktkurs, km Jülich 25 September

5 Jülich ObservatorY for Cloud Evolution JOYCE aims at investigating the processes of cloud formation and cloud evolution (precipitation) Various instruments set up at the Research Centre Jülich continuously monitor winds, temperature, water vapor, clouds, and precipitation over many years geomet.uni-koeln.de/joyce JOYCE: Scientific goals Goals to disentangle water vapor variations due to advection and to local surface influence validate coupled models to better understand the development of boundary layer clouds including cloud radiation interaction to observe precipitation formation and improve parametrization schemes 5

6 JOYCE: Instrumente (24/7) Scanning cloud radar MIRA-36s Micro Rain Radar Lidar Ceilometer Scanning MWR HATPRO Doppler Sodar Pulsed Doppler Lidar Infrared spectrometer AERI Total sky imager Sun photometer Radiation sensors Auxilliary instruments: 120-m meteorological mast, MAX-DOAS, GPS, polarimetric weather radar How to remotely sense cloud parameters? Active and passive techniques in different spectral regions use extinktion, absorption and scattering of electromagnetic radiation to indirectly sense cloud properties Clouds are best visible in atmospheric windows Microwaves (radiometry, radar & GNSS) Thermal infrared (satellite radiometry and spectrometry) Visible (reflected sun light, lidar, sun photometer) 6

7 How to determine cloud occurrence? Total Sky Imager (Yes Inc.) Specifications: Camera looks from above on spherical mirror Sun is blocked by black tape on mirror Temporal resolution 20 s Products & retrievals: Cloud classification based on RGB components for each pixel (in-house algorithm): sky, thin- and opaque clouds (blue, light blue and white) Cloud fraction Total Sky Imager Advantages: very reliable, intuitive, spatio-temporal structure Disadvantage: difficult to interprete due to geometry effects 18 UTC 12 UTC 06 UTC 10 June 2011 N E S W 7

8 At which height do clouds occur? Lidar Ceilometer CT25K Specifications: pulses at 905 nm temporal resolution 15 s range resolution ~15m, range 0-7 km Products & retrievals: senitive to small particles cloud base height optical extinction assuming constant lidar ratio (in-house algorithm) aerosol layer height Lidar ceilometer Advantages: very reliable, vertical structure Disadvantage: does not penetrate liquid water (cloud!) Altitude (m above ground) Aerosol Rising PBL Ice clouds Rain Fog 8

9 Remote sensing and sensor synergy Lidar - backscatter coefficient prop. r 2 - depolarisation information (phase!) - strong extinktion by water clouds Cloud radar - radar reflectivity factor Radar Lidar Z = D 6 N(D) dd Lidar Radar - Doppler-spectrum - linear depolarisation ratio LDR - influence by insects and drizzle Height LWC -liquid water content Cloud radar Cloud JOYCE Sends (active!) out pulses of microwave radiation Measures backscattered radiation 35 GHz) Time between emitted and received pulse information on the distance to backscatterer Sensitive towards cloud droplets, ice particles & precipitation Doppler radar radial velocity component can be measured Doppler spectrum can help to distinguish different targets Polarized receiver target discrimination constraint information on particle shape 9

10 Cloud radar radar reflectivity factor :30-14:30 Doppler velocity 95 GHz GKSS cloud radar MIRACLE Lineare depolarisation ratio backscatter proportional r 6 Doppler Cloud Radar MIRA-36 Elevation scan from 90 to 15 10

11 Sensor Synergy: target categorization Bit0: small liquid cloud drops (SCD) Bit1: falling hydrometeors Bit2: wet-bulb temperature < 0 C Bit3: melting ice Bit4: aerosol Bit5: insects Only mean to derive complex vertical structure of multi-level, multi-phase clouds Provides assumptions for radiative transfer and retrieval algorithms Why is cloud liquid water so important? Liquid water path (LWP) observations Jiang et al,

12 MicroWave Radiometer (MWR) HATPRO TOPHAT: Measures thermal emission of atmospheric gases and liquid water Brightness temperatures (TB) in 14 channels measurements Azimuth and elevation scanning Complete hemispheric scans during 7 min Products: Temperature and humidity profiles Integrated Water Vapor (IWV) Liquid Water Path (LWP) Microwave radiometry Standard atmosphere temperature profile water vapour profile liquid water path liquid water path LWP=250 gm -2 scattering at cloud droplets is negligle in microwave spectral region extinction l absorption α T B = T Bcos exp( τ )+ T( s) α(s) exp( α(s')ds') ds 0 s 0 12

13 Cloud radar and microwave Interruption for scanning radar reflectivity factor doppler velocity But how to get the liquid water content profile? spectral width The inverse problem Remote sensing instruments measure indirect information, e.g. the measurement vector y includes radiances TB at different frequencies Forward problem (radiative transfer) for a given atmospheric state x (temperature, humidity, cloud parameter) is well constrained y = F(x) Microwave spectrum Atmosperic profile Inverse problem (retrieval algorithm), i.e. the determination of the atmospheric state is often ill-conditioned and requires the inclusion of empirical information 13

14 Integrated Profiling Technique measurement 1 + error a variational approach towards multiinstrument retrieval measurement 2 + error measurement 3 + error a priori information + error Inversion OPTIMAL ESTIMATION Löhnert et al., 2004 and 2008 atmospheric state: temperature, humidity, hydrometeors + errors Liqud Water Content (LWC) Application of LWC retrieved by IPT for evaluating regional climate models Models show different liquid water paths and different peak altitudes 14

15 Further developments in synergy Combination of ground-based and satellite information spatial representation of supersites Development of a quasi-real-time variational algorithm based on optimal estimation theory Meteosat Seviri Z R SW TB IR TB MW TB IR I IR Integration & minimization of cost function Profiles of T, q, LWC, r eff Cloud radar MRW IRR AERI Challenges in sensor synergy Goall: synchroneous scans radar microwave radiometer 15

16 Summary and conclusions Clouds clouds have a strong effect on the Earths energy and water budget cloud processes are rather complex and involve scales from nm to km cloud feedbacks related to aerosols and changes in temperature and humidty are not well understood Observations better observations of clouds are urgently required sensor synergy observations and modelling need to be linked closely for further progress How to sense the various cloud parameters? Macro-physical Parameter cloud fraction cloud height cloud contours 3D-structure Radiative Quantities extinction coefficient ε [m -1 ] optical thickness τ z= τ λ = ε λ ( z') d z' z= 0 transmission t = exp(-τ) Micro-physical Parameter number concentration N effective radius r eff liquid water content LWC radar reflectivity Z Ice- and mixed phase clouds phase shape density 16