Measuring High-Latitude Precipitation from Space

Ralf BennartzSSEC University of Wisconsin – Madison

EES – Vanderbilt University

Outline

• What can we do now?

• Connecting to the surface

• Greenland mass balance

• Validation

• Clouds and the surface energy balance

• Way forward

Contributions

• IWSSM-4 participants, in particular:

• Tristan L’Ecuyer

• Mark Kulie

• Gail Skofronick-Jackson

• Karen Boening

• Anne Walker

• Deb Vane

IWSSM-4Fourth International Workshop on Space-based Snowfall Measurement

• Held March 2013, Mammoth

• 50 participants

• Reports back to IPWG, CGMS, WMO

• Detailed recommendations from four working groups:

1. Applications & Validation

2. Radiative Properties

3. Global & Regional Detection/Estimation

4. Missions & Concepts

Ralf Bennartz, University of Wisconsin Robin Hogan, University of ReadingPaul Joe, Environment Canada Gail Skofronick Jackson, NASA GSFCGraeme Stephens, JPLDeb Vane, JPL Jeff Dozier, UCSB

Phase

C. Kidd

NEXRAD 1638Z Terra MODIS 1635Z

Type

Observing Snow with CloudSat

Active sensor Excellent sensitivity Near-global coverage Coincident measurements from other A-Train sensors

Strengths of CloudSat:

Complex relationship between reflectivity and snowfall rate/IWC Rain/snow discrimination Sampling Ground Clutter

Challenges:

T. L’Ecuyer

Global Precipitation Measurement (GPM)

• Joint NASA/JAXA mission

• 2013: As soon as NASA find a launch vehicle…

• Active: Dual-frequency Precipitation Radar (DPR) 12/17 dBZ MDS

• Passive: Multi-frequency GPM Microwave Imager (GMI): 13 channels (10-183 GHz)

Drag picture to placeholder or click icon to add

GPM - Courtesy of NASA GSFC

Global Precipitation Measurement (GPM)

• GPM-DPR

• “It is estimated that due to its higher detectability threshold, only about 7%/1% of the near-surface radar reflectivity values and about 17%/4% of the total accumulation associated with global dry snowfall would be detected by a DPR-like instrument”

(Kulie & Bennartz 2009, from global CloudSat observations)

10Earth Explorer User Consultation Meeting 19 and 20 April 2004 EarthCARE 10

EarthCARE

FOUR INSTRUMENTS:DOPPLER CLOUD RADARHIGH SPECTRAL RESOLUTION LIDARMULTI SPECTRAL IMAGERBROAD BAND RADIOMETER

A. Illingworth

Greenland Ice Sheet: Surface Mass Balance

M. van den Broeke et al. 2009

CloudSat data density

• Even nadir-only sensors (CloudSat) provide decent coverage at higher latitudes

• About 7000 obs per year per 1x2 deg box at 70 N.

CloudSat: 2006 – 2010 Mean

Greenland Ice Sheet: Surface Mass Balance

• Cloudsat derived annual mean precipitation accumulation GIS 6/2006:

• 650 Gt/yr

GPCC: 2006 – 2010

GPCC: 2006 – 2010

Snowflakes

Summit Observatory – Greenland Shupe et al. (2012)

Vermont – Bentley (ca. 1902)

Modeling Non-spherical Particles

• In the last 5 years significant progress has been made in modeling non-spherical ice optical properties

• What did we learn?

• How can we constrain them?

Uncertainty due to habit

Hiley, Kulie, Bennartz (JAMC, 2011)

Comparison to Canadian Surface Observations

Jet Propulsion LaboratoryCalifornia Institute of Technology

Workshop on Space-based Snowfall Measurement (4th IWSSM), May 6-8, 2013Boening et al.: SNOWFALL-DRIVEN MASS CHANGE ON THE EAST ANTARCTIC ICE SHEET

Ice Mass Increase and Antarctic Precipitation

CloudSat

• CloudSat observes snowfall

• Accumulated snow from CloudSat agrees well with GRACE’s observed mass

• Sublimation plays minor role

This slide from Boening et al.

Clouds and surface melting

ICECAPS observations and models show that clouds were “just right” for enhanced ice

surface melt in July 2012.

Thin liquid water clouds trap infrared radiation while still permitting enough solar radiation to

lead to maximum surface heating.

• Polar precipitation is a major gap with current satellite missions. Given the changes that are being observed in the Arctic due to climate change, a future satellite mission must focus on the capability to address polar precipitation and clouds synergistically in order to address climate and water cycle science/prediction priorities.

• There is much to gained by the precipitation and snow on the ground communities working together to outline priorities for future missions and address retrieval issues.

• If we want to observe climate signals, we need to have long-term records. Need for stable, well-calibrated long-term satellite datasets.

Conclusions