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NESDIS Snowfall Rate Product and its Applications Part 1: Huan Meng 1 , Jun Dong 2 , Cezar Kongoli 2 , Ralph Ferraro 1 , Banghua Yan 1 1 NOAA/NESDIS/Center for Satellite Applications and Research 2 ESSIC/Cooperative Institute for Climate and Satellites – Maryland Part 2: Kris White 3 , Emily Berndt 3 , Bradley Zavodsky 3 3 NASA/MSFC/Short-term Prediction Research and Transition Center JPSS Seminar, November 20, 2017

NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

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Page 1: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

NESDIS Snowfall Rate Product and its Applications

Part 1: Huan Meng1, Jun Dong2, Cezar Kongoli2, Ralph Ferraro1 , Banghua Yan1

1NOAA/NESDIS/Center for Satellite Applications and Research

2ESSIC/Cooperative Institute for Climate and Satellites – Maryland

Part 2: Kris White3, Emily Berndt3, Bradley Zavodsky3

3NASA/MSFC/Short-term Prediction Research and Transition Center

JPSS Seminar, November 20, 2017

Page 2: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Project Overview

• A project supported by the JPSS PGRR

Program;

• Project period: 2015-2017

• Objective:

Enhancement of the ATMS and AMSU/MHS

land Snowfall Rate (SFR) product

Expansion of the SFR suite to support

applications

• Algorithm enhancement

Snowfall detection

Snowfall rate

• SFR product from new sensors/satellites

SSMIS aboard DMSP F16, F17, and F18

GMI aboard NASA GPM

Composite NEXRAD

Reflectivity

Retrieved Snowfall

Rate

2

Page 3: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

ATMS and MHS SFR

• ATMS: Advanced Technology Microwave

Sounder

AMSU: Advanced Microwave Sounding Unit

MHS: Microwave Humidity Sounder

ATMS is the successor to AMSU/MHS

• AMSU/MHS SFR is a NOAA operational

product, four satellites: NOAA-18,-19 and

Metop-A,-B (AMSU/MHS)

• Compared to AMSU/MHS, ATMS has improved

sampling configuration and more channels,

especially two more water vapor channels

suitable for precipitation retrieval.

• S-NPP ATMS SFR was developed with the

support of the JPSS PGRR Program

• ATMS SFR outperforms AMSU/MHS SFR

AMSU/MHS ATMS Ch GHz Pol Ch GHz Pol

1 23.8 QV 1 23.8 QV

2 31.399 QV 2 31.4 QV

3 50.299 QV 3 50.3 QH

4 51.76 QH

4 52.8 QV 5 52.8 QH

5 53.595 ± 0.115 QH 6 53.596 ± 0.115 QH

6 54.4 QH 7 54.4 QH

7 54.94 QV 8 54.94 QH

8 55.5 QH 9 55.5 QH

9 fo = 57.29 QH 10 fo = 57.29 QH

10 fo ± 0.217 QH 11 fo±0.3222±0.217 QH

11 fo±0.3222±0.048 QH 12 fo±

0.3222±0.048

QH

12 fo

±0.3222±0.022

QH 13 fo±0.3222±0.022 QH

13 fo±

0.3222±0.010

QH 14 fo±0.3222

±0.010

QH

14 fo±0.3222±0.004

5

QH 15 fo±

0.3222±0.0045

QH

15 89.0 QV

16 89.0 QV 16 88.2 QV

17 157.0 QV 17 165.5 QH

18 183.31 ± 1 QH 18 183.31 ± 7 QH

19 183.31 ± 3 QH 19 183.31 ± 4.5 QH

20 191.31 QV 20 183.31 ± 3 QH

21 183.31 ± 1.8 QH

22 183.31 ± 1 QH

(Table by Bill Blackwell)

3

Page 4: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Algorithm Methodology

Two major components in the SFR algorithm

• Snowfall Detection (SD)

Statistical approach (Kongoli et al., 2015, 2017)

Coupled principal component and logistic regression model

• Snowfall Rate

Physical model (Meng et al., 2017; Ferraro et al., 2017)

1DVAR-based retrieval

• Issues

Weakened signal from shallow-cloud snowfall and snowfall with

supercooled cloud liquid water due to emission effect

Missed snowfall

Underestimated snowfall rate

4

Page 5: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

SD Enhancement (1/3)

Previous SD model - satellite module

Predictors: High frequency channels above 89 (MHS)/88.2

GHz (ATMS), 5 from MHS and 7 from ATMS; 3 principal

components

Two temperature regimes: warm and cold, defined by

53.6 GHz

Model output is the probability of snowfall

Training data sets are composed of matching satellite data

and ground snowfall observations, i.e. ‘truth’ data

5

Page 6: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Environment-based weather module

Logistic regression model

Input data: RH, V-Vel, CThick

Blended model: The SD algorithm is an optimal combination of

the satellite module and weather module

Additional model-based screening to improve the accuracy of the

SD algorithm

SD Enhancement (2/3)

Example of Shallow-Cloud Snowfall

6

Page 7: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

The combined SD improves statistics for

both shallow and thick-cloud snowfall

ATMS SD statistics against gauge

snowfall observations

*The statistics for the satellite-only SD algorithm

62% of ground truth data is ‘trace’, i.e. very light

snowfall – very challenging to detect from satellite

observations

Previous SD

Radar Reflectivity

Combined SD

SD Enhancement (3/3)

Warm Regime Cold Regime

POD (%) 51 (41*) 52 (30)

FAR (%) 5 (4) 9 (6)

HSS 0.5 (0.43) 0.42 (0.28)

7

Page 8: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Snowfall Rate Algorithm (1/2)

1D variational method

Forward simulation of Tb’s with a radiative transfer model (RTM)

(Yan et al., 2008)

Iteration scheme with ΔTBi thresholds

IWP and De are retrieved when iteration stops

Ic: ice water path

De: ice particle effective diameter

i: emissivity at 23.8, 31.4, 89(MHS)/88.2(ATMS), 157/165.5, and 190.31/183±7 GHz

TBi: brightness temperature at 23.8, 31.4, 89/88.2, 157/165.5, and 190.31/183±7 GHz

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

E: error matrix

176/190

165/157

88/89

31

23

1

176/190

165/157

88/89

31

23

)(

B

B

B

B

B

TT

e

c

T

T

T

T

T

AEAA

D

I

8

Page 9: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Terminal velocity is a function of atmospheric conditions and

ice particle properties, Heymsfield and Westbrook (2010):

Snowfall rate model (Meng et al., 2017):

,

An adjusting factor, a, to compensate for non-uniform ice

water content distribution in cloud column; derived from

collocated satellite and StageIV radar and gauge combined

hourly precipitation data

Snowfall Rate Algorithm (2/2)

9

Page 10: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Snowfall Rate Enhancement

Ice only: r = 0.44

MRMS

Recalibration using NSSL Radar Multi-

Sensor (MRMS) snowfall data as ‘truth’

Better spatial (via integration) and

temporal compatibility than StageIV

The radiative transfer model (RTM) in the

current SFR algorithm does not include

the effect of cloud liquid water

The RTM has been modified to include

CLW

Leads to increased SFR in most cases –

mitigate the dry bias in SFR

Developing more robust initialization of

cloud properties

Ice + liquid: r = 0.50

10

Page 11: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

• A major nor’easter swept through the Mid-

Atlantic and the Northeast on March 14-15,

2017

• The ATMS and MHS SFR products captured the evolution of the snowstorm with five satellites including S-NPP, POES and Metop.

March 14-15, 2017 Nor’easter 24-hour snowfall accumulation

ending March 15, 2017 12 UTC

NOHRSC

SFR

(Courtesy of Patrick Meyers)

Corr Coeff

Bias (mm/hr)

RMS (mm/hr)

ATMS 0.68 0.05 0.71

MHS 0.65 -0.13 1.01

11

Page 12: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Courtesy of Patrick Meyers

ESSIC/CICS-MD

12

Page 13: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Expand SFR to using DMSP SSMIS and NASA GMI sensors

Snowfall is highly dynamic

Essential to utilize all available passive microwave sensors with

high frequencies to improve SFR temporal resolution

SFR Expansion

GPM

MOA

MOB

N18

F18

S-NPP/JPSS-1

{

F16 N19

F17

13

Page 14: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

SSMIS SD (1/2)

• SSMIS is aboard three DMSP satellites:

F16, F17, and F18

• Conical scanning imager/sounder; different from

ATMS and MHS which are cross scanning

sounders

• SD model for individual satellite Channel failure

Different sensor characteristics

• Similar algorithm framework as ATMS SD

Satellite-based module

Blended algorithm: Satellite module + weather

module

Additional screenings

• Ground observations as training data set

Channel Freq (GHz) Pol FOV

1 19.35 H 45x74

2 19.35 V 45x74

3 22.235 V 45x74

4 37 H 28x45

5 37 V 28x45

6 50.3 H 37.5

7 52.8 H 37.5

8 53.596 H 37.5

9 54.4 H 37.5

10 55.5 H 37.5

11 57.29 RC 37.5

12 59.4 RC 37.5

13 63.283248 ±

0.285271 RC 75

14 60.792668 ±

0.357892 RC 75

15

60.792668 ±

0.357892 ±

0.002

RC 75

16

60.792668 ±

0.357892 ±

0.0055

RC 75

17

60.792668 ±

0.357892 ±

0.016

RC 75

18

60.792668 ±

0.357892 ±

0.050

RC 75

19 91.665 H 13x16

20 91.665 V 13x16

21 150 H 13x16

22 183.311 ± 1 H 13x16

23 183.311 ± 3 H 13x16

24 183.311 ± 6.6 H 13x16

14

Page 15: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

SSMIS SD (2/2)

• Performance against ground observations

POD (%) FAR (%) HSS

F16 57 17 0.41

F17 48 15 0.34

F18 55 16 0.38

• Algorithm difference from ATMS SD

Satellite-based module: Logistic regression model; no principle

analysis due to less water vapor sounding channels

52.8 and 53.6 GHz significantly improve F16 and F18 model

performance

One temperature regime

15

Page 16: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

SSMIS SFR

Physically-based SFR algorithm, similar to ATMS

SFR

1DVAR to retrieve cloud properties

Modified RTM: bias correction, scattering property LUT

Frequencies: 22, 37V, 91V, 150 (F16 and F17), 183±6.6 GHz

Algorithm for individual satellite

Algorithm calibration

NSSL MRMS

SFR and MRMS spatial collocation through convolution

Time lag by 30 min based on statistical analysis

Histogram matching

Corr Coeff Bias

mm/hr

RMS

mm/hr

F16 0.49 -0.10 0.93

F17 0.57 -0.07 0.85

F18 0.43 0.00 0.99

16

Page 17: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

GMI SD

• GMI is the radiometer aboard NASA

Global Precipitation Mission (GPM)

core satellite

• Conical scanning radiometer with high

spatial resolution

• Similar algorithm framework as ATMS

SD model

Blended algorithm

Two temperature regimes

Channel Freq (GHz) Pol IFOV

(km x km)

1 10.65 V 19x32

2 10.65 H 19x32

3 18.7 V 11x18

4 18.7 H 11x18

5 23.8 V 10x16

6 36.5 V 9x16

7 36.5 H 9x16

8 89 V 4x7

9 89 H 4x7

10 166 V 4x6

11 166 H 4x6

12 183.31±3 V 4x6

13 183.31±7 V 4x6 Warm Regime Cold Regime

POD (%) 68 56

FAR (%) 15 14

HSS 0.55 0.45

17

Page 18: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

GMI SFR

Physically-based SFR algorithm

1DVAR to retrieve cloud properties

Modified RTM: bias correction, scattering property LUT

Frequencies: 23, 36V, 89V, 166V, 183±7 GHz

Algorithm calibration

NSSL MRMS

SFR and MRMS spatial collocation through convolution

Time lag by 30 min based on statistical analysis

Histogram matching

Corr Coeff Bias

(mm/hr)

RMS

(mm/hr)

0.52 -0.10 0.84

MRMS

GMI SFR

18

Page 19: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Towards a LEO+GEO SFR

Fusing LEO MW and GEO IR data

Take advantage of the accuracy of MW

product and the temporal resolution of

IR data

Track snowstorm movement between

LEO overpasses using GEO data

Identify trend in snowfall intensity

between LEO overpasses using GEO

data

GOES-R3 project

Part of the CICS GOES-R3 Total Water

project

Performance period: FY17-FY18

Explore the fusion of SFR and GOES-

16 products and imagery

SFR overlaid on GOES IR with METAR reports (Courtesy of Bill Line)

17:05Z

19:40Z

19

Page 20: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Radar and Satellite Merged SFR (mSFR)

• Merging MRMS instantaneous snowfall product and SFR provide better

spatial and temporal (10-min) coverage and ability to loop the data (mSFR)

• Developed with the support of NASA/SPoRT

20

Page 21: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Radar and Satellite Merged (mSFR) m

m/h

r (liquid

equiv

ale

nt)

21

Page 22: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

• Hydrology

Most blended satellite precipitation datasets do

not include satellite snowfall rate product – use

other data sources (model, ground

observations, etc.)

CMORPH is a NOAA global blended

precipitation analysis product with wide-ranging

applications

The first generation CMORPH only has rain

rate. The SFR product is integrated in the

second generation pole-to-pole CMORPH

• Weather forecasting

Fill observational gaps in mountains and

remote regions where radar and weather

stations are sparse or radar blockage and

overshooting are common

Provide quantitative snowfall information to

complement snowfall observations or

estimations from other sources (stations, radar,

GOES imagery data, etc.)

Applications

(Xie and Joyce, NOAA/NCEP/CPC)

Stage IV

Radar Precip

2nd Generation

CMORPH

Snowfall

Rainfall

22 22

Page 23: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

User Interaction and Support

Near real-time SFR and mSFR production at CICS-MD using

Direct Broadcast (DB) data from University of Wisconsin

Madison

SFR production within 30 min of observation

SFR and mSFR data provided to SPoRT where the products

are converted to AREA file and AWIPS and disseminated to

NWS WPC and some WFOs

Support SFR assessment

User training

Interaction with users during assessment

SFR webpages with near real-time images

DB-based: http://cics.umd.edu/sfr

Global: https://www.star.nesdis.noaa.gov/corp/scsb/mspps_backup

/sfr_realtime.html

23

Page 24: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Other Notes

ATMS SFR added to JPSS L1RD

Requirement for transition to operation

A year-long effort with approval from authorities such as SPSRB,

PCB, NOSC, and DUSO

S-NPP SFR will be integrated in the MiRS system and

transitioned to operation

Code modification to follow SPSRB standard

Pending PSDI review

Publications

Two published papers

Two conditionally accepted papers

SFR assessment in the coming winter

24

Page 25: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Summary

A blended snowfall detection algorithm was developed for ATMS and

MHS; it improves snowfall detection for both shallow- and deep-cloud

snowfall

The radiative transfer model has been modified to include cloud

liquid water; ongoing work on the initialization of cloud properties

Developed the SSMIS snowfall detection and snowfall rate

algorithms; adding three DMSP satellites

Developed the GMI snowfall detection and snowfall rate algorithms;

adding NASA GPM satellite

ATMS SFR has been added to JPSS L1RD; S-NPP SFR will be

transitioned to operation inside MiRS in the near future

Product assessment in winter 2017-2018 for the full-suite of SFR

from nine satellites; effort led by SPoRT with participation from NWS

WFOs

25

Page 26: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Application and Assessment of NESDIS Snowfall Rate

Kris White, Emily Berndt, Brad Zavodsky

NASA SPoRT

JPSS Science Seminar – Part 2

20 November 2017

Page 27: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

End User Interactions

Winter 2014 assessment: January to mid April 2014

Goal: determine operational utility in the forecaster

environment as it relates to:

radar gaps

beam blockage/overshooting

tracking snowfall rate maxima (in combination with other satellite

imagery)

Participating Offices

Albuquerque, NM

Burlington, VT

Charleston, WV

Sterling, VA

Satellite Analysis Branch

27

Page 28: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Product in 2014

AMSU/MHS from NOAA-18, -19, MetOp-A, -B

Up to 8 SFR retrievals per day at mid-latitudes

Land only retrievals

Limited to regions with surface temperature > 22°F

30 to 90 minutes time lag between retrieved snow and snow reaching the ground

Detectable snowfall rates from 0.004 in/hr to 0.2 in/hr (2 in/hr if snow to liquid ratio is 10:1)

Processed near real-time at NOAA/NESDIS with a 30 min to 3 hour latency

SPoRT made data AWIPS/NAWIPS compatible for dissemination to NWS

Module and Quick Guide developed

28

Page 29: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

Heavy snowfall events in warmer temperature regimes were

captured well

13-14 April 2014 a fast-moving upper level trough and a

backdoor cold front moved south from the eastern plains of

Colorado to New Mexico

“The 0429Z SFR product

has the greatest values

observed in NM for this

event. Our Clayton observer

(CAO – orange oval) did call

in at 06Z with a report of 1.5

inches of snow. We didn’t

receive snowfall at the Las

Vegas ASOS (LVS – pink

oval). Another spotter call

from 05Z reported 2.5

inches of snow at a location

near the purple arrow.” –

Albuquerque, NM WFO.

29

Page 30: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

Inability to detect lighter snowfall amounts that were detected

by ASOS

“It looks like the SFR

product did not detect all of

the snow that was falling

around 11 UTC. But the

misses can either be

described as either (1) the

surface temperature being

too cold or (2) the

probabilistic model that is

part of the calculations,

indicating probabilities were

too low to determine if there

was snow” – Charleston,

WV WFO.

30

Page 31: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

10:47 UTC, no snow at BHM (red oval)

1619 UTC, snowing at BHM (black circle)

• Under the right conditions, SFR can be used as a short term forecast

product

• In-cloud snow not reaching the surface can seed existing clouds to

increase the likelihood of snow reaching the surface

At 1047 UTC, SFR showed

snow in clouds but no snow

was observed at surface in

Birmingham; in-cloud seeding

was occurring

At 1553 UTC, snowfall was

reported at BHM and later

intensified; SFR product, IR,

and NEXRAD all reported

snow by 1619 UTC

More details:

https://nasasport.wordpress.co

m/2014/02/24/birmingham-

alabama-surprise-snow-of-

january-28-2014-or-was-it/

31

Page 32: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Feedback Summary

Product was limited at times by its latency but forecasters

found it valuable in a operational sense and to validate

snow reports

Overall feedback was positive with more than 75% of

responses indicating the product was useful to improve

data coverage in areas with radar gaps and in

combination with satellite observations to track snowfall

maxima

Limitations were uncovered

Lighter snowfall rates not detected

Sometimes missing snowfall captured by radar

32

Page 33: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Recommendations to Developers

Reduce the latency to < 60 minutes

Explore improving the low snowfall rate detection

efficiency (an increased false alarm rate may be

acceptable if more events are captured)

Investigate the ability to retrieve snowfall rates when

surface temperatures are colder. This would make it

easier for northern WFOs to use the product and enable

use in Alaska

33

Page 34: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

End User Interactions

Winter 2016 assessment: January to February 2016

Goal: determine operational utility in the forecaster environment as it relates to: radar gaps

beam blockage/overshooting

tracking snowfall rate maxima (in combination with other satellite imagery)

Determine areas where cloud seeding may be occurring ahead of falling precipitation

Participating Offices Albuquerque, NM

Anchorage, AK

Juneau, AK

Boulder, CO

Charleston, WV

Sterling, VA

34

Page 35: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

New Product Developments

Added ATMS

Alaska product developed

Generated from Direct Broadcast data to reduce latency to within 30 minutes

Snow to liquid ratio options

10:1

18:1

35:1

Merged snowfall rate product

Polar orbiter swath complimented with NSSL’s Multi-Radar/Multi-Sensor precipitation data

Product updated every 10 minutes

Put in image 1 animation from

the blog

https://nasasport.wordpress.co

m/category/nesdis-snowfall-

rate/page/2/

35

Page 36: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

Rain to snow transition

event 15 Feb. 2016

where rainfall mixed

with and then

transitioned to all snow

“Much of the precipitation across

West Virginia was still in the form

of rain…with an area of snow

extending from northwest PA

across Ohio into southwest

portions of the state. There

appears to be several

observations of rain across Ohio

with surface temperatures of 32

to 35 DegF where the SFR

product indicated snow in the

clouds” – Charleston, WV WFO.

36

Page 37: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

Enhanced snowfall

detection in areas with

lack of radar coverage

– case from NW New

Mexico, 4-5 Jan 2016

“The arrival of a SFR product at

0010 UTC 5 January

2016 showed the extent of the

precipitation was much greater

with the merged POES image

overlaid on the radar data.” –

Brian Guyer, WFO ABQ

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Page 38: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

Although more the 75% of responses indicated low to

medium confidence in SFR values, 75% of responses

indicated the product was useful

Most responses indicated SFR was used to identify

snowfall in data-deprived regions

Reasons the SRF product was not useful:

Underestimated snowfall amount

Not available over water or coastline

Missed location of light/moderate snow detected by other sources

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Page 39: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Results

More the 85% of responses indicated medium to high

confidence in merged SFR values, 100% of responses

indicated the product was useful

75% of forecasters indicated the ability to loop the product with

blended radar made the product more useful

Most responses indicated merged SFR was used to identify

snowfall in data-deprived regions and track the maxima

Reasons the merged SRF product was not useful:

Underestimated snowfall amount

Still too latent

Missed location of light/moderate/heavy snow detected by other

sources

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Page 40: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Upcoming Assessment Winter 2017-18

Changes to product

Inclusion of SSMIS (DMSP: F16, F17, F18) and GMI (aboard NASA GPM)

Improved snowfall detection algorithm

Goal: Determine operational utility in the forecaster environment as it relates to:

Temporal resolution of data/imagery

Accuracy of snowfall detection and rates based on type of snowfall event

radar gaps

beam blockage/overshooting

tracking snowfall rate maxima (in combination with other satellite imagery)

Determine areas where cloud seeding may be occurring ahead of falling precipitation

Participating Offices

Albuquerque, NM

Anchorage, AK

Boulder, CO

Charleston, WV (limited participation)

Great Falls, MT (waiting to hear)

Juneau, AK (waiting to hear)

Fairbanks, AK (waiting to hear)

Sterling, VA (waiting to hear)

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Page 41: NESDIS Snowfall Rate Product and its Applications...NESDIS Snowfall Rate Product and its Applications Part 1: Huan 2Meng 1, Jun Dong , Cezar Kongoli 2, Ralph Ferraro , Banghua Yan1

Summary

SPoRT and NESDIS have collaborated over the last 4

years to introduce the snowfall rate product to NWS

forecasters and assess the utility in the operational

environment

User feedback has let to product improvements including

a merged product, availability of liquid to snow ratio

displays, and inclusion of additional polar-orbiting data

Successful story of R2O and O2R with a period of

intensive interaction between product developers and

end-users.

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