Remote Sensing of Precipitation, Cloud, Aerosol and Water Vapor Using Radar, Lidar and Microwave

Radiometer

Presentation at IMD, Delhi October 1, 2012

2

Overarching proposition: The physical and dynamical processes key to MJO initiation are closely connected to the unique features of the tropical Indian Ocean (e.g., monsoon flows, thermocline ridge,

Wyrtki jets) and must be adequately understood using local observations.

Goal: Expedite our understanding of MJO initiation processes and efforts to improve simulation and prediction of the MJO

Objectives: – Collect observations (field campaign)

– Establish empirical statistics; prepare data for model constraints, validation, and evaluation (analysis)

– Test hypotheses; identify model deficiencies; provide better physical basis for model improvement (modeling)

– Develop prediction indices for MJO initiation; benchmark improvement in MJO prediction (forecast)

DYNAMO (Dynamics of the MJO)

The US Participation in CINDY2011 (Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year 2011)

October 1, 2011 – March 31, 2012

Land-based operational sounding site

R/V Mirai R/V Ron Brown R/V Southern Surveyor

R/V Sagar Kanya

Land-based enhanced sounding site

R/V Revelle

10S

Eq

65E 75E 85E

Gan

Diego Garcia

NE

SE

RAMA

Hypotheses: Three essential factors for MJO initiation

I. Interaction between convection and its environmental moisture

II. Distinct roles of different types of convective clouds at each MJO initiation stage

III. Upper ocean processes and air-sea interaction

Central Problem: Maintenance and transition of different MJO initiation stages

A B C

Conceptual Model for MJO initiation: Pre-onset stage (A): Convectively suppressed; recharging

with deepening moist layer, aided by shallow clouds Onset stage (B): Convectively active, with both shallow and deep (including stratiform) convective clouds; deep

moist layer, maintained by low-level moisture supply Post-onset stage (C): strong surface wind and entrainment

cooling; deep convection declining due to low SST

Facility Platform Period Hypothesis Testing

S-PolKa radar Gan IOP I, II

SMART radar Gan EOP I, II

AMF2 Gan EOP I, II

ISS Diego Garcia IOP I, II

GAUS/wind profiler US ship IOP I, II

TOGA radar US ship IOP I, II

aerosol US ship IOP II

surface flux, Doppler lidar, cloud radar

US ship IOP I, II, III

upper-ocean mixing US ship IOP III

surface current and temperature drifters EOP III

surface meteorology and upper-ocean profiles

moorings IOP III

Summary of DYNAMO Observations

IOP: October 1, 2011 – January 15, 2012 EOP: October 1, 2011 – March 31, 2012

Gan

Sup

er si

te

Indian Ocean region for DYNAMO

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India

Male

Maldives

Sri Lanka

Addu Atoll

Diego Garcia

Addu Atoll

Gan Island

habitat islands linked by the road

Wharf

Cloud particles

Light rain

Moderate rain Heavy rain

Drizzle

Hail

Rain/hail mix

Graupel/small hail Graupel/rain

Dry snow

Wet snow Oriented ice crystals

Irregular ice crystals Super cooled liquid drople

Insects

Birds Ground clutter

Reflectivity (dBZ)

Differential Reflectivity (dB)

Particle classification

S-PolKa Radar • Mass, Latent Heating Rates,

Profiles • Hydrometeor Identification

• Detection of cloud droplets

• Raindrop size

distribution

• Effect of Bragg scatter is less at

Ka-band

• Improved cloud microphysical

retrieval (precipitation type,

shape, size and concentration)

using both dual-wavelength and dual-polarization

observations

Objectives

• Retrieve path-integrated humidity – Differential gaseous absorption – Compare reflectivity at nearest edge of

cloud – Create profile by plotting mid-point of

path integrated estimates

Range

Hei

ght

Range resolved cloud liquid

Path integrated water vapor profiles

+ +

+

• Retrieve range-resolved liquid water content (LWC) and median volume

diameter (MVD) through clouds – Differential absorption through

clouds

+ +

Method: Humidity retrieval

• Run radiation model many times varying T, P and specific humidity (SH, g m-3)

• Compute polynomial fit of SH to attenuation Sp

ecifi

c hu

mid

ity (g

m-3

)

1-way atm attenuation (dB km-1)

SH = 201.40A3 – 209.60A2 + 120.55A – 2.25

Where SH is specific humidity (g m-3) and A is gaseous attenuation (dB km-1)

Results from RICO

+ radar retrieval – primary ray + Radar retrieval – secondary ray

- Sounding

Without dry layer

With dry layer

RMS difference: sounding (g m-3)

0.85 1.40

Detection of Cloud Droplets/Ice Using IMD S-band Radars

Minimum detectable signal (dBZ) with SNR = 3 dB versus range for no attenuation losses (blue), 0.1 dB km-1 (red) and 0.2 dB km-1 (green) gaseous attenuation loss (2-way).

LWC from radar alone

• Z = 0.34LWC1.42 (gm-3) • Physical basis • Assumes no ice!!!!!

From Vivekanandan et al. 1999

Modeled Gamma DSD’s

Sample Plane Parallel Indicator (PPI) Scan From WISP04, 2004.

Method: Liquid retrieval

• Liquid water attenuation at Ka-band (Aka) linearly related to LWC (g m-3) – No dependence on drop size distribution – Small temperature correction (CT)

• MVD (mm) retrieved from LWC and reflectivity (Ze, mm6 m-3)

LWC = 0.74*Aka*CT

MVD3 = 2.16 x 10-4*Ze/LWC

Comparison of liquid water content between radar/radiometer retrievals and in-situ measurements during WISP04. In-situ measurements are from a liquid water probe on board the UND Citation research aircraft.

+ In-situ

Radar

Liquid water content, g m-3

Alti

tude

, m

Comparison of dual-wavelength radar and radiometer-derived liquid water path for 22 March 1991 from 0900 to 0912 GMT.

National Center for Atmospheric Research

Azimuth (deg)

Path

-inte

grat

ed

Liq

uid

Wat

er C

onte

nt (m

m)

LWC and MVD retrievals

S-band reflectivity (dBZ) LWC (g m-3)

Distance from radar (km) Distance from radar (km)

LWC ~ 0.05 – 0.1 g m-3

Rain and biological scatterers:dBZ of insects

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Rain and biological scatterers: ZDR of insects

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Rain and biological scatterers: Velocity of insects

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Rain and biological scatterers: dBZ of bird

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Rain and biological scatterers: ZDR of birds

25

Rain and biological scatterers: Velocity of birds

26

Rain and biological scatterers: Propagation Phase of birds

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Is it possible track insects as they migrate over the oceans?

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HSRL on the NCAR Gulf Stream-V Research Aircraft

Technical Specifications: Wavelength: 532 nm Pulse repetition rate: 6 KHz Average power: up to 400 mW Range resolution: 7.5 m Telescope diameter: 40 cm Angular field of view 0.025 deg Filter bandwidth: 1.8 GHz

Background on Lidar Calibration

a. Relative calibration: Transmit power, optics and receiver

b. Absolute calibration: Rayleigh molecular backscatter

c. Automatic self-calibration

-Integrated baclkscatter, B= 1/2S - S: lidar ratio =extinction/backscatter - Measurements from a thick startocumulus clouds that have droplets sizes between 5 and 25 microns - S doesn’t vary for cloud droplets - No drizzle or ice particles - Backscatter > 1.e-5 1/(m Sr) - No strong background aerosol: attenuation in BL increase S

d. Similarity to radar calibration using phase measurements

GV HSRL measurements of aerosol and smoke at NCAR’s Foothills campus on September 6, 2010

GV HSRL measurements of aerosols and clouds

Backscatter Measurement

Depolarization Measurement

Particle Types

Backscatter Measurement

Depolarization Measurement

Particle Types

Model calculations of scattering.

Model calculation: Median volume diameter verses Lidar Ratio

Particle Types

Analysis of Calibration Results

Status Median Volume

Diameter, microns

Lidar ratio: gate-by-gate

ratio of extinction to backscatter

Integration of attenuated backscatter

Raw backscatter measurements, Beta

18 22 26

Adjusted backscatter measurements: Beta’=Beta*1.22

17.4 19.2 22

• GV HSRL Designed and built by University of Wisconsin – Madison

• Provides accurate measurement of optical depth, extinction and backscatter cross sections of aerosols and thin clouds

• Eye-safe at the exit port (532-nm wavelength operation)

• GV HSRL is already operational operation as ground based instrument

• To be used to in combination with HCR to measure: –cloud fraction, precipitation rate, scattering cross sections, particle shape

measurements on 1-Oct of 2008 by the arctic HSRL and MMCR Courtesy of University of Wisconsin

High Spectral Resolution Lidar and mm-wave radar

Instruments • Ground-based radar: (i) total phase or delay and (ii) absorption

using a dual-wavelength radar • Ground-based multi-wavelength microwave radiometer

• Satellite-borne microwave radiometer

•Differential absorption lidar and Raman lidars

•GPS receivers

Vertical distribution of water vapor (US Standard atmosphere)

Remote Observations of WV

• Cm and mm wave radars

• Microwave radiometer

• Differential absorption lidar (DIAL)

• Solar occultation

• GPS receivers

Radiometer Technique • Radiometers measure brightness

temperatures Tb, that are converted into optical depths, τ.

• Optical depths are linearly

related to LWP and VWP • kl and kv are path averaged

coefficients. • τd is the ‘dry’ optical depth • Two wavelengths, two equations,

two unknowns – retrieve LWP and VWP.

Times series measurements of temperature, integrated vapor and liquid

Spatial and Temporal Resolutions (a) Radiosondes: • Synoptic scale; twice daily. Thermodynamic vertical profiles for

weather diagnostics and prediction

• Not suitable for resolving microscale and mesoscale features of minutes to hours and 1-10 km scale

(b) Satellite: Crude vertical resolution within boundary layer (c) Radiometer: Continuous observations to fill temporal gaps between

radiosondes (i) Microwave: Measurements during both cloudy and clear air (ii) Infrared: Biased in cloudy condition

Ground-based radiometer

Radiation Transfer Equation

Radiation transfer equation:

Source:

Rayleigh-Jeans limit to Planck function:

Planck function or intensity and temperature:

α: Absorption coefficient

Boundary conditions for the solution of RT equation

Radiation Transfer Equation (Cont.)

Solution to the above:

Optical depth:

Microwave radiometry:

Profiling Using a Microwave Radiometer

Frequency of the emitted radiation Bohr’s Equation

Total internal energy of a molecule:

Two important rotational transitions: 22.235 and 183.31 GHz

Brightness Temperature and weighting function

TB : Brightness temperature W(f,z) : Weighting function g(z): Water vapor density

Sensing Using Passive instruments

Comparison of RAOB and CMR-H Retrieved Water Vapor Density Profile on Oct 9, 2007

09 Oct 07 at 6 UT. A priori from Denver Station RAOB at 0 UT

CMR-H RAOB PWV (cm) 0.85 0.78

Radiometer retrieval of temperature, RH, and liquid water content during a supercooled fog event

(Knupp et al., JAOT 2009)

Summary • Radiometer is capable of providing high temporal

estimates of water vapor.

• Inherent spatial or vertical resolution of water vapor estimate is limited.

• Vertical resolution of the water vapor can be improved by the following:

(i) Narrow-beam radiometer (ii) 1-D Var method (iii) Including scanning measurements to vertical pointing

observations (iv) Tradeoff between std. error in WV and spatial resolution (v) Tomography - multiple radiometers

Motivations: (i) Very limited prediction skill for MJO initiation over the Indian Ocean; (ii) The inability of global models to produce the MJO, which degrades their seasonal to interannual prediction and lessens our confidence in their ability to project future climate.

Correlation between predicted (by CFS) and observed MJO indices (Courtesy of Jon Gottschalck and Qin

Zhang)

Importance of the MJO: Bridging Weather and Climate

• Monsoons, ENSO • Extreme events (flood, tropical

cyclones) • Teleconnections, extratropical

circulation/weather • North Atlantic Oscillation, Arctic

Oscillation, Antarctic Oscillation • Atmospheric and oceanic chemistry and biosystem (ozone, CO2, aerosols,

chlorophyll)

Tracks of hurricanes (1947-1997) in two contacting phases of the MJO (Maloney and

Hartmann 2000)

Rain and biological scatterers: Propagation Phase of insects

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Corrections for finite beamwidth and mean radiating temperature

T’B : Corrected brightness temperature

Backscatter cross section as a function of time and altitude.

Depolarization ratio as a function of time and altitude