Retrieving Snowfall Rate from Satellite Measurements

Huan Meng1, Ralph Ferraro1, Banghua Yan1, Cezar Kongoli2, Jun Dong2, Nai-Yu Wang2, Limin Zhao1

1NOAA/NESDIS2Earth System Science Interdisciplinary Center, UMCP

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Why do Snowfall Rate Retrieval

• Importance of snowfall rate data Snowfall is a major form of precipitation in mid and high latitudes Many applications: weather forecast, aviation, hydrology, water

resources, transportation, agriculture, climate studies, etc. Provide much needed winter precipitation estimates to blended

global precipitation products

• Inadequate snowfall observations/estimations Few snowfall data sources Gaps in radar and station coverage Quality issues with radar and station snowfall data

• Development of satellite snowfall rate product falls far

behind rain rate products Many existing satellite rainfall products with a wide range of

applications

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Satellites and Sensors

Channel Frequency(GHz)

Channel Frequency(GHz)

A1(W) 23.8 A8(O) 55.5

A2(W) 31.4 A9-A14(O) 57.290*

A3(W) 50.3 A15(W) 89.0

A4(O) 52.8 M1(W) 89.0

A5(O) 53.6 M2(W) 157

A6(O) 54.4 M3-M4 (H)/M5 (V)

183.31*/190.31

A7(O) 54.9

• Satellites: NOAA and EUMETSAT polar-orbiting satellites: NOAA-18, NOAA-

19, Metop-A, and Metop-B • Sensors: Advanced Microwave Sounding Unit-A (AMSU-A); Microwave

Humidity Sounder (MHS) Twenty combined channels, 23 – 190 GHz Frequencies are respectively sensitive to temperature, water vapor and surface Cross track scanning, variable footprint size

AMSU-A1 AMSU-A2 MHS

NOAA-18

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Pattern of Satellite Measurements

• Each satellite orbits the earth 14 times a day Usually overpasses each location twice a day, once going from the

south to the north or ascending, and once from the north to the south

or descending, with about 12 hours between the two overpasses Each orbit takes 102 minutes, i.e. neighboring orbits are 102 min apart Takes 2-3 orbits to cover CONUS

Ascending Overpasses1:30 PM LST Equator Crossing Time

Descending Overpasses1:30 AM LST Equator Crossing Time

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AMSU/MHS Snowfall Rate Product

• Satellite retrieved liquid equivalent snowfall rate (SFR) over land

Need snow to liquid ratio to convert to solid snow

The ratio is dependent on local climatology and environmental conditions such as temperature and water vapor profiles

Typical snow ratio is 10:1 but can range from 5-25:1 or an even wider range

• Four polar-orbiting NOAA POES and EUMETSAT Metop satellites can provide eight SFR retrievals a day in mid-latitudes that are grouped into 4 morning overpasses and 4 afternoon overpasses

• SFR resolution is 16 km at nadir and 26 km x 52 km at limb

• Maximum liquid equivalent snowfall rate is 0.2 in/hr (i.e. 2 in/hr solid snow with a 10:1 ratio); minimum is 0.004 in/hr (will miss very light snow)

Radar Reflectivity

SFR

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Snowfall Rate Images

• AWIPS SFR files from SPoRT for a productevaluation project

• Research webpage: http://www.star.nesdis.noaa.gov/corp/scsb/mspps_backup/sfr_realtime.html

Near real-time images with time stamps

Both SFR and rain rate images are available. The latter can be used to find orbital gaps

Access to an archive of images going back to January 1, 2012

Research page: no real time support; algorithm updates periodically time stamp – represents the time the

satellite overpasses the middle latitude line of the image, e.g. 37.5 for this image

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Algorithm Methodology

1. Detect snowfall areas

2. Retrieve cloud properties with an inversion method

3. Compute snow particle terminal velocity and derive

snowfall rate

Snowfall Detection (1/2)

· Start with two products: AMSU snowfall detection (SD) (Kongoli et al., 2003) and AMSU rain rate (RR) (Ferraro et al., 2005; Zhao and Weng, 2002). Recently, a new SD algorithm has been developed and implemented.

· Apply filters (based on GFS T and RH profiles) to RR and SD to determine snowfall (Foster, et al., 2011)

· Both SD and RR algorithms rely on the scattering signal caused by ice particles to detect precipitation. Scattering depresses the microwave measurements. The SFR algorithm employs the same principle.

NMQ (radar) Phase SFR w/ the new SD

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NMQ snow only

NMQ all precipitation

POD = 73%

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Snowfall Detection (2/2)

· Snow event in the Midwest, February 1, 2011. Comparison of satellite retrieved snowfall (rain retrieval not included) and ground observation of snow vs. rain

The algorithm has the ability to differentiate between rain and snow correctly in most cases.

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Retrieval of Cloud Properties

· Inversion method Simulation of brightness temperatures (Tb) with a Radiative

Transfer Model (RTM) (Yan et al., 2008) Use multiple channels

Iteration scheme with ΔTbi thresholds

Ic and De are retrieved when iteration stops

Ic: ice water path

De: ice particle effective diameter

i: emissivity at 23.8, 31.4, 89, 157, and 190.31 GHz

TBi: brightness temperature at 23.8, 31.4, 89, 157, and 190.31 GHz

A: derivatives of TBi over IWP, De, and i

E: error matrix190

157

89

31

23

1

190

157

89

31

23

)(

B

B

B

B

B

TT

e

c

T

T

T

T

T

AEAA

D

I

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Snow Particle Terminal Velocity

· Model by Heymsfield and Westbrook (2010):

: dynamic viscosity of air, Re: Reynolds number, a : air density,

D: maximum dimension of the ice particle

Re = f(Ar), area ratio Ar = A/(D2/4); Ar = 1 for

spherical ice particle

Assume spherical ice particles in this study

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Snowfall Rate

· Assumptions about snow microphysical properties

Spherical habit, number density of ice particles follows exponential

distribution, fixed ice particle density

An adjusting factor to compensate for non-uniform ice water content

distribution in cloud column

· Model

,

Integration is solved numerically

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MHS Tb Weighting Functions

· Each frequency has its unique weighting functionC1, C2, and C5 are sensitive

to ice scattering and are used in the SFR algorithm

Weighting functions for these channels peak at different atmospheric levels

By using these channels, SFR represents the snowfall in an integrated precipitation layer rather than at the surface

C1: 89 GHzC2: 157 GHzC3: 183.31±1 GHzC4: 183.31±3 GHzC5: 190.31 GHz

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Christmas Day SnowstormDec 25-27, 2010

SFR (mm/hr)

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Validation of AMSU/MHS SFR

· Validate over contiguous United States

· Validation Sources Station hourly accumulated precipitation data

StageIV radar and gauge combined hourly precipitation data

National Mosaic & Multi-Sensor QPE (NMQ) instantaneous radar

precipitation data

· Validation challenges Spatial scale difference with station data: 16+ km footprint vs. point

measurement Temporal scale difference with station/StageIV data: instantaneous vs. hourly Other issues with ground observations and radar snowfall data

Quality issues: station data with undercatch (underestimation) due to dynamic effect; NMQ data with positive bias.

StageIV/NMQ: Range effect, overshooting, beam blockage, overestimation at the presence of melting snowflakes, etc.

Validation with StageIV and Station Data

· Five large-scale heavy snowfall events from 2009-2010 Jan 27-28, 2009 Mar 23-24, 2009 Dec 08-09, 2009

Jan 28-30, 2010 Feb 05-06, 2010 The events cover

diverse geographic

areas and climate

zones 16

Case Study: Dec 8-9, 2009 (1/3)

· A large snowstorm system in the Midwest Heavy snowfall (1’+) in NE, IA, MN, WI

Blizzard condition, winds > 45 mph

Before After

Case Study: Dec 8-9, 2009 (2/3)

· Time series of SFR areal mean vs. StageIV and station data Mean SFR evolution agrees well with Stage IV and station data Mean SFR is generally lower than the validation data except with

station data at the later stage of the storm

· Scatter plots further supports theagreements Station vs SFRStageIV vs SFR

Case Study: Dec 8-9, 2009 (3/3)

· Histogram

· Statistics Validation

SourceBias

(in/hr)RMSE(in/hr)

CorrCoe

StageIV -0.0094 0.0240 0.49Station -0.0001 0.0291 0.23

Images of matching data

SFR & StageIV SFR & Station

StageIV

SFR

Summary Statistics

  Bias(in/hr)

RMSE(in/hr)

Correlation Coefficient

StageIV -0.0091 0.0264 0.42

Station 0.0028 0.0283 0.30

· Combined statistics from all five events

· Sanity check - Comparison between StageIV and Station SFR generally has better statistics against each of the validation

sources than between the two sources

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Validation with NMQ Data (1/2)

· Validation with NMQ instantaneous radar precipitation rate

NMQ (Q2) radar only data: 0.01 degree, every 5 minutes Better comparability in spatial (radar weighted average) and temporal

collocations between satellite and radar NMQ has considerable positive bias against StageIV

Satellite NMQ (Radar)

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Time series of areal mean snowfall rate from satellite and radar

Time series of bias and correlation coefficient between satellite and radar snowfall rate

Validation with NMQ Data (2/2)

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Product Applications

· Identify snowstorm extent and the location of the maximum intensity within the storm

· Provide quantitative snowfall information to complement snowfall observations or estimations from other sources (stations, radar, GOES imagery data etc.)

· Fill observational gaps in mountains and remote regions where weather stations are sparse and radar blockage and overshooting are common

· Locate snowstorms at higher latitudes where the quality of the subjective GOES IR and VIS imagery data deteriorates

· Track storms and derive trending information (e.g. strengthening or weakening of the storm) by pairing with GOES IR/VIS/WV images to

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What to be Aware of

· This is a liquid equivalent snowfall rate· There usually is a time lag between the retrieved SFR and

the best correlated ground observation, due to the slow terminal velocity of snow particles

Satellite microwave signal can penetrate cloud, so SFR represents snowfall throughout the precipitation layer

· The current product is limited to regions where the surface air temperature is about 22°F and above

Extension to colder climate is currently under development

· Not applicable to lake effect snow The resolution of the product is too coarse to detect the narrow snow band of

lake effect snow

· Misses very light snowfall due to a minimum SFR limit· Polar-orbiting satellite product with latency of 30 min – 3 hrs

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Use Case 1 – Tracking Snowstorm

How do I use the SFR Product?

• SFR adds additional information to radar and GOES data

• SFR makes it easy to identify edge of snowfall and area of maximum intensity

• The movement and strength of a feature identified in SFR can be tracked using radar or GOES imagery between satellite overpasses

• In addition, radar and GOES images can also be used to infer storm trending information

Difficult to tell rain from snow in radar

SFR Product at 17:05Z

SFR Product at 19:40Z

Hard to determine snowing clouds from GOES

Snow edge and max intensity easy to infer Radar at 17:06ZGOES IR image at 17:00Z

Snow max later rotated and moved north

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Use Cases 2 & 3 – Filling Radar Gaps

SFR retrieval over an area in radar gap during a snow event in Newfoundland, Canada on November 29, 2012

NEXRAD Composite Reflectivity

NEXRAD Composite Reflectivity & SFR

xxxx

Mono LakeLee ViningJune Lake

Mammoth Lakes

Higher elevation rain does not appear on SFR product

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SFR from Next Generation Microwave Sensor

· Advanced Technology Microwave Sounder (ATMS) Follow-on microwave sensor to AMSU and MHS Onboard S-NPP (launched in October 2011) and the future JPSS

satellites More snow-sensitive channels and better sampling configuration

than AMSU/MHS for improved snowfall retrieval

• ATMS SFR Complete baseline algorithm by the end of 2013 Currently under final calibration and validation Increase SFR retrievals to 10 times a day, 5 in the morning and 5 in

the afternoon

· Future development Extension to colder climate Improvement to SFR utility

Preliminary ATMS SFR Validation

· ATMS Snowfall Rate Show agreement with AMSU/MHS

SFR in basic patterns Might have higher false alarm rate, still

under validation Preliminary validation result:

AMSU/MHS, 8:37Z

ATMS 8:05Z

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Product Summary

•SFR is liquid equivalent snowfall rate•SFR uses observations from microwave sensors aboard

polar orbiting satellites•Currently eight SFR retrievals a day grouped into 4

morning and 4 afternoon overpasses•ATMS SFR is under validation. Will add 2 more SFR

retrievals a day•Most important SFR applications:

Identify snowstorm extent and area with the most intense snowfall

Fill gaps in radar coverage and ground observations

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Thanks!