Satellite-Based Monitoring of Snow Cover

at NOAA: Application to Himalayan-

Tibetan Region

Peter Romanov

NOAA-CREST, City University of New York

Center for Satellite Applications and Research, NOAA/NESDIS

NOAA: National Oceanic and Atmospheric Administration, USA

NESDIS: National Environmental Satellite Data and Information Service of NOAA

Outline

• NOAA/NESDIS snow mapping/monitoring activities

• Snow products: Application in mountainous regions

Snow cover: Needs and Requirements

Requirements to snow products

• Daily, Spatially continuous

• Continental or global scale coverage

• 1-4 km resolution

Surface observations are not enough, satellite data should be used

NOAA needs information on snow for

• Weather prediction

• Climate studies

• Hydrological forecasts, flood warnings

Snow cover: Techniques

Satellite-based snow mapping techniques used

• Interactive

• Automated

- Visible & infrared

- Passive microwave

- Combined visible-infrared-microwave

Focus on operational polar and geostationary satellites & sensors

NOAA, GOES, NPP, METOP, Meteosat, GCOM, MTSAT, DMSP

On the Web: http://www.natice.noaa.gov/ims/

Interactive snow mapping

- Based on visual analysis of

satellite imagery in optical bands

- Yes/no classification (snow/land,

water/ice)

- Maps available since early 1970s

Temporal sampling and spatial

resolution:

1972-1997 : weekly at 180km

1998-2003 : daily at 24 km

2004-2014 : daily at 4 km

2015 - : twice daily at 1 km NOAA Interactive Multisensor Snow and Ice

Mapping System (IMS)

Interactive snow/ice maps: example

Snow: white

Ice: yellow

Background:

elevation

- Interactive snow/ice maps are spatially continuous

- Cloudy areas: analysts make reasonable guess or use in situ data

- Effective spatial resolution may be coarser than nominal

Pamir-Tien Shan region

Snow cover from visible/infrared data

- Automated algorithms applied to AVHRR, VIIRS, SEVIRI sensor data

- Land/snow, water/ice, cloud categories

- High spatial resolution: 1 km and below

- High retrieval accuracy

- Requires daylight

- Gaps due to clouds (~40% of the area)

Metop AVHRR Snow: white

Ice: yellow

Clouds: gray

Background:

elevation

Mar, 15 Mar, 19 Mar, 22

Clouds

Snow temperature: snow melt identification

GOES-East Imager data

Snow-free land Red: Melting Snow

Based on visible and

infrared observations

Timely snow melt

identification may be

hampered by persistent

clouds

Snow retrievals from microwave

5 km

- Spatial resolution: 25-50 km

- Weather independent, mostly continuous coverage

- Problems: mountains, melting and shallow snow

- Sensitive to snow depth and snow water equivalent

- But retrieval errors are 50-100%

Snow water equivalent from

AMSR-E Aqua

On the Web: http://www.orbit.nesdis.noaa.gov/smcd/emb/snow/HTML/multisensor_global_snow_ice.html

Combined visible-infrared-microwave

- Automated algorithm, uses strengths of both techniques

- Spatially-continuous maps of snow and ice cover

- Nominal spatial resolution : 4 km

- Effective resolution varies depending on the source of data used.

Most satellite products agree to surface observations of snow

at the rate of over 90%.

Vis/IR: 93-98% agreement (but only for cloud-clear scenes)

Interactive: 90-95%

Combined: 90-95%

Microwave: 80-90%

How well satellite snow extent maps agree to surface observations ?

Snow cover duration

Based on daily snow extent maps

Used in climate change studies

2008-2009 Derived from IMS data

Snow extent change: 1972-2013

Estimated yearly mean snow extent decrease rate in NH is ~1.7% per decade Largest decrease occurred in 1980s

1970 1980 1990 2000 2010

Year

0

5

10

15

20

25

30

Are

a,

mln

sq

km

Northern Hemisphere

Eurasia

North America

Feb 22, 2012

VIIRS snow cover product at 0.5 km spatial Specific problems of snow mapping in mountains

- Topographical shadowing

- Geo-registration may be less accurate

- Lack of ground-truth for validation

- Microwave snow products are not reliable

Different snow products over Tibet

AVHRR False Color AVHRR (Automated)

IMS (Interactive)

Closer look reveals some

differences between products…

Interactive analysts tend to

overestimate snow extent

Clouds: gray Snow: white

Dec 25, 2014

Feb 22, 2012

VIIRS snow cover product at 0.5 km spatial Scaling issue

- Algorithms and analysts typically map pixel with any

amount of snow in it as “snow covered”

- As a result, snow extent in finer spatial resolution products

is smaller than in the coarser resolution products

- This effect is most pronounced in the mountains

Snow occurrence from interactive maps

1972-1997 week 33

180 km

1998-2003 week 33

24 km

2004-2014 week 33

4 km

Snow occurrence on week 33

(Aug 13-19)

Snow occurrence calculated at 180

km spatial resolution

Trend in snow occurrence in the

mountains areas is mostly spurious

Its is caused by different spatial

resolution of base snow maps

All products are available through NOAA web sites

Most reliable/accurate retrievals are of snow extent. Limited

ability to derive snow depth or SWE.

All products have some “inertia”: None of the sensors can “see”

through precipitation clouds

Differences in snow products are due to different techniques,

data sources, spatial resolution, time of observation.

All NOAA snow mapping techniques are continental/global. For

regional studies use global products with care

- Regionally-tuned algorithms may produce better results

Final notes

Links

NESDIS Automated snow remote sensing page:

http://www.star.nesdis.noaa.gov/smcd/emb/snow/HTML/snow.htm

NOAA Interactive snow charts:

http://www.natice.noaa.gov/ims/

NESDIS Microwave remote sensing page:

http://www.star.nesdis.noaa.gov/corp/scsb/mspps/

NOAA Satellite Data Archive:

http://www.class.noaa.gov

THANK YOU

Snow and Ice cover 2013-2014

Based on daily maps generated with NOAA Global Multisensor Automated Snow and Ice Mapping System (GMASI)