• Significant portion of yearly precipitation in the cold regions (including the polar regions) – important indicator of climate change and variation
• Input to winter snowpack and spring snowmelt runoff in mountain and polar regions – critical element of basin water cycle and regional water resources
• Influence on large-scale land surface radiation and energy budget particularly during accumulation and melt seasons
• Effect on glacier/ice sheet accumulation/mass balance, lake/river and sea ice, seasonal frozen-ground and permafrost
• Impact to human society and activity, such as air/ground transportation, disaster prevention, agriculture, water resources management, and recreation…

gauges, satellite and radar
Gauge network
• Global coverage with various operational, national/regional networks.
• Manual and automatic gauges, measuring water equivalent
(amount), not snow particle size.
• Manual gauges can measure snowfall (rate) at 6-hour to daily time intervals, and auto gauges can provide hourly (or sub-hourly) snowfall (rate).
• Snow rulers are also used for snowfall observations at the national/regional networks, providing snow depth info, not SWE.
• Snow pillow/snowboard/snow depth sensor record snow accumulation changes over time - (in)direct info of snowfall.
•
Gauge networks/data are long-term and fundamental, defining global snowfall/climate regimes and changes.

– Global coverage with merging data / products from IR, MV sensors and satellite radars
– Rain rate info (TRMM), also snowfall rate ???, challenge with mixed precip
– Particle size info from radars
– Operational products - GPCP blended version 2 monthly/global, 1987-present, and others????
– Problems of MV data over land, need systematical evaluation particularly over the high latitudes
– Limited validations show GPCP v2 data are not better than atmospheric reanalysis precip over northern regions (Serreze et al., 2005)
–
Statement of importance – Key to advance our capability of monitoring and observing (liquid/solid) precipitation globally???

Dataset Name
(Reference)
CMORPH
(Joyce et al. 2004)
Examples of RS Precip Dadasets
Data Sources and Merging Method Online Documentation Spatial & Temporal
Resol. and Coverage
0.25
o grid, 60
°
S - 60
°
N,
180
°
W - 180
°
E; 30 min.,
12/2002-present
Microwave estimates from the DMSP 13, 14 & 15
(SSM/I), the NOAA-15, 16 & 17 (AMSU-B) and the
TRMM (TMI) satellites are propagated by motion vectors derived from geostationary satellite infrared data.
http://www.cpc.ncep.noaa.gov/produ cts/janowiak/cmorph_description.ht
ml
PERSIANN
(Hsu et al. 1997)
TRMM 3B42
(Huffman et al. 2005)
0.25
o grid, 50
°
S - 50
°
N,
180
°
W - 180
°
E; 30 min.,
3/2000-present
A neural network, trained by TRMM TMI (2A12) precipitation, was used to estimate 30 min. precipitation from infrared images from global geosynchronous satellites.
http://hydis8.eng.uci.edu/persiann/
0.25
o grid, 50
°
S - 50
°
N,
180 ° W - 180 ° E; 3-hourly,
1/1998-present
Microwave (TRMM, SSM/I, AMSR and AMSU) precipitation estimates were used to adjust IR estimates from geostationary IR observations.
http://daac.gsfc.nasa.gov/precipitatio n/TRMM_README/TRMM_3B42
_readme.shtml
Merged microwave only precipitation
(X. Lin 2006, personal comm.)
2.5
o grid, up to 75
°
S -
75 ° N, 180 ° W - 180 ° E; hourly,
12/1997-present
Estimates from TRMM TMI, SSM/I on DMSP F13,
F14, F15, and
AMSR-E from AQUA were first averaged on a 0.25
o grid and then further averaged to a 2.5
o grid.
NCEP National Stage II multi-sensor hourly precipitation analysis
~4.8 km grid, continental
U.S.; hourly,
5/1996-present
About 140 WSR-88D radars over CONUS, and ~3,000 automated gauge reports were used in the analysis. http://www .emc.ncep.noaa.gov/mmb
/ylin/pcpanl/stage2/
– Operational products - GPCP blended version 2, monthly/2.5x2.5 grid, global, 1987-present

Summary Table: current/planned capabilities and requirements for space-based remote sensing of snowfall parameters (adopted from xxx, not done yet)
Measurement Range
Parameter
Snowfall amount
Precip/Snowfall rate
Precipitation type
Snow particle size
H
100
100
100
100
100
100
---
3
3
~0.7
1
1
0
0
0
0
L
0
0
0
0
0
Non e
0.3
0.3
C
T
O
C
T
O
C
C
T
O
T
O
C
T
O
U mm mm mm mm/ hr mm/ hr mm/ hr
---
Measurement
Accuracy
V
1
0.25
?
2-10
3
2
---
10
7
6-35
10
6
U mm mm mm cm cm
%
% cm
---
0.1
---
0.5
0.1
25
0.5
0.1
0.5
0.1
25
0.5
V
1
Spatial
U km
Resolution
V
Temporal
U day km km km km
1
12
1
6 day hr day day km
--km km km km km
12
---
1
6
1
6
12 hr
--day hr day day hr
Comment /
Principal
Driver
MODIS/SSM
I
Hydromet
AMSR-
E/TRMM
Hydromet
Transportatio n
Need HF
SAR
Hydromet e.g. AMSR-E
Hydromet
????
C = Current Capability L = Low end of measurement range U = Unit
T = Threshold Requirement (Minimum necessary) H = High end of measurement range V = Value
O= Objective Requirement (Target)

– Only cover very limited parts of the globe (much less extensive than the gauge network)
– Expensive and can be difficult to operate and calibrate
– Mainly designed for severe weather detection, with less concern for precipitation, certainly NOT for snowfall measurements, (although being used to measure snowfall with problems for light snowfall)
– Major limitations for operational radars:
• lack of low level coverage at moderate (80 km) to long range for precipitation and this is even shorter for snowfall
• in complex terrain, the radar beam is often blocked by mountains and/or the radar is located to scan over the top of mountains and not in the valleys
– A new innovation is the deployment of a network of redundant low cost, low maintenance radars (CASA radars) to scan the low levels of the atmosphere.
–
Statement of importance - key to understand cloud/precipitation physics and for validation of satellite precipitation data and products.

• gauge network and data
•
RS snow data validation

• Sparseness of the precipitation observation networks in the cold regions.
• Uneven distribution of measurement sites, i.e. biased toward coastal and the low-elevation areas, less stations over mountains and oceans.
• Spatial and temporal discontinuities of precipitation measurements induced by changes in observation methods and by different observation techniques used across national borders.
•
Biases in gauge measurements, such as wind-induced undercatch, wetting and evaporation losses, underestimate of trace and low amount of precipitation, and blowing snow into the gauges at high winds
• Data access is also difficult or costly for some regions and countries
• Decline of the networks in the northern regions/countries

Synoptic/climate stations on land above 45

N and the Arctic Ocean drifting stations
Canada
Greenland
Russia
Mongolia
China
Kazakhstan

NRCS National
Water and Climate
Center www.wcc.nrcs.usda.gov/snotel/Alaska/alaska.html

NOAA US CRN http://www.ncdc.noaa.gov/oa/climate/uscrn/

Canadian
Nipher
Russian
Tretyakov
US 8”
Hellmann

WMO double fence intercomparison reference (DFIR) in Barrow, AK D. Yang, 1998: WMO solid precipitation measurement intercomparison, final report,
WMO/TD-No. 872, WMO,
Geneva, 212pp.

120
100
80
60
40
20
0
0
Canadian Nipher
NWS 8" unsh
Tretyakov
NWS 8" Alter
Hellmann unsh
1 2 3 4 5 6
Wind speed at gauge height (m/s)
7 8 9

35
30
25
20
15
10
5
0 trace wind-loss measured
1 2 3 4 5 6
M onths
7 8 9 10 11 12
40
30
20
10
0
80
70
60
50
1 2 trace amount wind correction measured
3 4 5 6
Month
7 8 9 10 11 12
Overall mean for the NP drifting stations,
1957-90 (Yang,
1999)
Overall mean for 61 climate stations in
Siberia, 1986-
92 (Yang and
Ohata, 2001)

45
40
35
30
25
20
15
10
5
0
15
10
5
0
30
25
20 trace w etting loss w ind loss measured measureable trace

Comparison of Bias Corrections in the High Latitudes
3.00
2.50
2.00
1.50
1.00
0.50
Siberia Greenland Arctic basin Alaska NWT
Regions

150
Mean Gauge-Measured (Pm) and Bias-Corrected (Pc)
Precipitation, and Correction Factor (CF) for January
Yang et al., 2005, GRL
1
2
0
1
2
0
1
2
0
6
0
6
0
6
0
30
150
30
150
30
-15
0
-1
2
0 a) Pm (mm)
-6
0
0
Pm (mm)
0 - 10
10 - 20
-30
30 - 40
40 - 50
50 - 60
60 - 70
70 - 80
80 - 90
90 - 330
-15
0
0 180
-1
2
0 b) Pc (mm)
-6
0
Pc (mm)
0 - 10
10 - 20
-30
30 - 40
40 - 50
50 - 60
60 - 70
70 - 80
80 - 90
90 - 390
-15
0
-1
2
0 c) CF
-6
0
CF
1 - 1.1
1.1 - 1.2
-30
1.3 - 1.4
1.4 - 1.5
1.5 - 1.6
1.6 - 1.7
1.7 - 1.8
1.8 - 1.9
1.9 - 2.3
0
• Total 4827 stations located north of 45N, with data records longer-than 15 years during 1973-2004.
• Similar Pm and Pc patterns – corrections did not significantly change the spatial distribution.
• CF pattern is different from the Pm and Pc patterns, very high CF along the coasts of the Arctic Ocean.

150
1
2
0
Mean Gauge-Measured (Pm) and Bias-Corrected (Pc)
Precipitation, and Correction Factor (CF) for July
Yang et al., 2005, GRL
6
0
1
2
0
1
2
0
6
0
6
0
30
150
30
150
30
-15
0
0
Pm (mm)
0 - 10
10 - 20
-30
30 - 40
40 - 50
50 - 60
60 - 70
70 - 80
80 - 90
90 - 250
-15
0
-1
2
0 b) Pc (mm)
-6
0
0
Pc (mm)
0 - 10
10 - 20
-30
30 - 40
40 - 50
50 - 60
60 - 70
70 - 80
80 - 90
90 - 300
-15
0
-1
2
0 a) Pm (mm)
-6
0
• Total 4802 stations with records longer-than 15 years during 1973-2004.
• Similar Pm and Pc patterns.
• Small CF variation for rainfall over space.
• CF pattern is different from the Pm and Pc patterns.
-1
2
0 c) CF
-6
0
CF
1 - 1.1
1.1 - 1.2
1.3 - 1.4
1.4 - 1.5
1.5 - 1.6
1.6 - 1.7
1.7 - 1.8
1.8 - 1.9
1.9 - 2
0

Impact of Bias-Corrections on Precip Trend
Pm & Pc Trend Comparison, Selected Stations with Data > 25 Yrs during 1973-04
-15
Jan.
-12 -9 -6 -3
15
12
9
6
3
0
-3
0
-6
-9 y = 1.2103x - 0.1012
R
2
= 0.9448
-12
-15
Pm trend (mm)
3 6 9 12 15
-7
Jul.
-6 -5 -4 -3 -2 -1
-4
-5
-6
7
6
5
4
3
2
1
0
-1
0
-2
-3
1
-7
Pm trend (mm)
2 3 4 y = 1.0575x
R
2
= 0.9962
5 6 7
Yang et al., 2005, GRL

- Comparison with in-situ snow data (scale issue)
- Regional / basin water budget calculations to assess moisture budget closure:

Basin/region winter snow mass balance
SWE = Snowfall – Sublimation

Basin spring water budget
Runoff = SWE + Precip. – Evaporation – Storage
Hydrologic modeling and snow assimilation

Large Arctic rivers & their annual discharge to the
Arctic
Ocean/marginal seas
5%
9%
Table 1: Physical characteristics for the five major rivers of the Arctic.
River Drainage
Name Area
(1,000 Km
2
)
River
Length
(Km)
Annual
Discharge
(Km
3
)
Mean Annual
Temperature
(

C)
Mean Yearly
Precipitation
(mm)
Snowfall
Percent
(%)
Total Res.
Capacity
Km3 / # dam
Ob
Yenisei
Lena
Yukon
Mackenzie
2,990
2,580
2,490
1,790
850
4,400
3,650
3,490
3,000
5,470
404
603
525
333
210
0.4
-4.3
-7.8
-5.1
-3.3
470
467
390
385
395
47
47
44
44
42
62 / 5
470 / 12
36 / 2
0 / 0
25 / 2
15%
17%
11%

Ob Basin Yenise Basin
50
1979,Q peak
=44,643m
3
/s
160
1990,Q peak
=157,286m
3
/s
200 Snow Water Equivalent (SWE) Information
140
40
120 150
30
20
10
0
0
1967,Q peak
=26,286m
3
/s
20 40
Modified Weeks
60
100
80
60
40
20
0
0
1968,Q peak
=64,771m
3
/s
20 40
Modified Weeks
60
100
50
0
0
Yukon Basin
1985,Q peak
=30,299m
3
/s
35
30
25
10
5
20
15
0
0
1978,Q peak
=12,969m
3
/s
20 40
Modified Weeks
60
35
30
25
10
5
20
15
0
0
Mackenzie Basin
20
1992,Q peak
=33,343m
3
/s
1995,Q peak
=15,086m
3
/s
40
Modified Weeks
60
Lena Basin
1989,Q peak
=177,429m
3
/s
1998,Q peak
=84,457m
3
/s
20 40
Modified Weeks
60
Basin extreme (weeklymean) discharge (m3/s).
Data source: UNH/SHI

Ob Yenise
140
Snow Water Equivalent (SWE) Information
120 1994,SWE max
= 114.2mm
140
120
140
120
100
80
60
40
1997,SWE max
= 75.2mm
100
2001,SWE max
= 97.8mm
80
60
40
1998,SWE max
= 76.2mm
100
80
60
40
Lena
2000,SWE max
= 136.4mm
1. Lena basin has the
1993,SWE max
= 109.2mm
highest winter snow pack, and
Yenisei basin has the lowest.
20 20 20
0 0.5mm
0 6 12 18 24 30 36 42 48 54
Modified Weeks
140
Yukon
1998,SWE max
= 124.7mm
0 0.5mm
0 6 12 18 24 30 36 42 48 54
Modified Weeks
140
Mackenzie
0
0
2. The snow pack
Modified Weeks accumulate to the highest in winter,
120 120
100
2001,SWE max
= 102.6mm
100
80
1991,SWE max
= 80.9mm
80
1993,SWE max
= 82.1mm
2. For study
60 60
40 40
20
0 0.5mm
0 6 12 18 24 30 36 42 48 54
Modified Weeks
20
0 0.5mm
0 6 12 18 24 30 36 42 48 54
Modified Weeks is considered
‘empty’.

0
150
75
0
75
0
150
150
75
1988
200 150
The SWE and Dicharge in Lena Basin, 1988~1999
200 150
1989 1990
200 150
100 75 100 75 100 75
1991
200
100
0 0
200 150
0 0
200 150
0 0
200 150
0
200
1992 1993 1994 1995
100 75 100 75 100 75 100
1996
0 0
200 150
100 75
0 0
1997
0 0
200 150
100 75
0 0
Weeks
1998
0 0
200 150
100 75
0 0
1999
0
200
100
0

350
300
250
200
150
100
1988-1989
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1992-1993
50
0
35 40 45 50 2 7 12 17 22
350
300
250
200
150
100
50
1996-1997
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
50
2000-2001
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1989-1990
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1993-1994
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
50
1997-1998
0
35 40 45 50 3 8 13 18 23
SWE
AP
Weeks
350
300
250
200
150
100
1990-1991
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1994-1995
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
50
1998-1999
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1991-1992
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
1995-1996
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
100
50
1999-2000
0
35 40 45 50 2 7 12 17 22

350
300
250
200
150
1988-1989
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1992-1993
100
50
0
35 40 45 50 2 7 12 17 22
350
300
250
200
150
1996-1997
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
2000-2001
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1989-1990
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1993-1994
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1997-1998
100
50
0
35 40 45 50 3 8 13 18 23
SWE
AP
Weeks
350
300
250
200
150
1990-1991
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1994-1995
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1998-1999
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1991-1992
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1995-1996
100
50
0
35 40 45 50 3 8 13 18 23
350
300
250
200
150
1999-2000
100
50
0
35 40 45 50 2 7 12 17 22

IPWG/GPM/GRP Workshop on Global Microwave Modeling and
Applications
• Climate
Retrieval of Snowfall, Oct.11-13, 2005
Co-Organizers: Ralph Ferraro (NOAA/NESDIS),
Ralf Bennartz (University of Wisconsin)
• Data assimilation and NWP
• Nowcasting, Short Range Forecasting, Severe weather
• Hydrology
Modeling and remote sensing of snowfall
• Particle optical properties
• Cloud microphysics modeling
• Forward modeling uncertainties
• Statistical and physical approaches for snowfall retrieval
New technology
• Active/passive sensor combination (CloudSat/AQUA), GPM, EGPM
• Microwave and sub-millimeter radiometers
• Ground based remote sensing
Validation
• Validation requirements for global satellite retrievals
• High latitude validation sites
• Field campaigns

– Network
• continue conventional point precipitation measurements against declining networks in many countries
• sustain and enhance the gauge network in the cold regions;
• develop guidelines on the minimum station density required for climate research studies on solid precipitation in cold climate regions
– Data
• undertake bias analysis and corrections of historical precipitation gauge data at regional to global scale
• ensure regular monitoring of the snowfall real-time data, quality control and transmission
• examine the impact of automation on precipitation measurement and related QA/QC challenges, including compatibility between national data, and manual vs. auto gauge observations
• develop digitized metadata for regional and national networks
– Test facility/new technology
• identify and establish intercomparison sites for standardized testing of new technology, such as polarization radar, CASA radar networks, hot plate, pressure, or blowing snow sensors
• encourage national research agencies to establish programs to provide support for the development of new instruments to measure solid precipitation in high latitude regions
• use of wind shields and direct measurement of winds at emerging auto gauge sites/networks

– Need GPM ASAP and strongly encourage the EGPM mission to measure global rain/snowfall data, including major parts of the N regions
– Need to blend (combine) data from different sources (in-situ, model, satellite)
– Need to systematically evaluate RS snow data / products over cold regions via direct comparisons, analyses of basin water budget and compatibility in basin/region SWE-runoff, SWE-snowfall
– Need to maintain reasonable expectations on what satellite and radar technologies are able to provide
– Need for further intensive field efforts to address scaling issues.
– Need for new technology development
• The use of combined active and passive satellite data for snowfall detection/retrieval should be further encouraged.
• Active space-borne instruments need to have a low detectability threshold (better than than 5 dBz) to detect light rainfall and snowfall. Deployment of rain radars with lower detectability threshold is encouraged.
• New passive microwave instruments and new channel combinations need to be studied, particularly at high frequency.
• The sounding channel technique proposed by the EGPM mission should be implemented.
• The new Meteosat Second Generation has many more channels than previous geostationary satellites. They have been able to provide information on particle size and phase. Exploration of these additional channels for precipitation estimation is encouraged.
• Aircraft sensors together with extended channel selection studies provide an excellent testbed for future satellite instruments. Dedicated high latitude aircraft campaigns for snowfall remote sensing are encouraged.

Ground Radar
– Need to expand the radar networks to the northern/cold regions and to obtain more useful radar observations of snowfall.
• The CASA radar concept should be deployed with high sensitivity for the detection of snow, low level measurements and in complex terrain.
– Need to share data and to create regional and global radar data sets
• international radar data quality intercomparisons to remove inter-radar biases of precipitation estimates.
• Availability of common or open source algorithms for generating precipitation estimates are needed to understand the biases and errors.
– Need for development and further refinement of inexpensive groundbased remote sensing instruments for snowfall should be encouraged, including vertically pointing micro radars, such as (Precipitation
Occurrence Sensing System) POSS or Micro-Rain-Radar (MRR).
– Encourage use of combined active and passive satellite data for snowfall detection/retrieval
– Need to study new passive microwave instruments and new channel combinations