NOAA NIC Snow Analysis and Blended Products

Sean R. Helfrich1, Min Li2, and Cezar Kongoli3

1 NOAA/NESDIS/OSPO/NIC, NOAA NSOF Building, 4231 Suitland Road, Suitland, MD 20746, sean.helfrich@noaa.gov

2 I.M. Systems Group, Inc. NOAA World Weather Building, 5200 Auth Road, Camp Springs, MD 20746

3 Cooperative Institute for Climate and Satellites, 5825 University Research Court, Suite 4001, M Square Building , University of Maryland, College Park, MD 20740

OUTLINE

• Background

• Current Products

• Future Products

• Conclusions

Introduction

• NOAA mapping snow since 1966.

• IMS used to generate first NOAA daily snow and ice product in 1997.

• Applied by NWP models, climate monitoring, satellite atmosphere retrievals, SSTs estimates, border patrol, drug enforcement, hydrologic mapping, wind mapping, soil moisture satellite estimates, etc.

MASIE

MASIE offers many regional

images and time series that

allow users to get data just

for their area of interest.

Screen Shot of GUI display

Input

Satellites

+

Other Sources

Radar Models Surface Obs Webcams Buoys Charts

Production Flow Outline

Pre Processing

Indirect Sources

Analyst Derived output

Output to Customers

IMS CONUS Validation

IMS Vs Surface SD

Chen et al., 2010 = % surface report and IMS Agreement

= # of NCDC stations

Current Capacities (2012)

Direct Input GOES (E & W)

MeteoSat (MSG & 7)

MTSAT

NOAA Automated Snow & Ice

AVHRR (Channels 1&3)

MODIS (Channel 8)

ASCAT

AMSU (Derived snow, ice, rain)

NIC Marginal Ice Zone

Surface Obs (METAR)

NOHRSC SNODAS

AFWA Snow Depth

SSMI/S (Derived snow, ice, rain)

MMAB Sea ice Cons

Indirect Input AVHRR (Channels 1,2,3a&b,5) MODIS (LANCE) OSCAT AMSU BTs RadarSat 1 & 2 Sea Ice Charts (NIC, CIS, DMI, SMHI, CG –

Japan, AARI)

Radar (USA, Canada, Europe, China) NWP models Foreign Snow analyses (Canada, Russia,

Japan, Slovakia, Germany, Norway, Sweden, Finland, etc)

COOP, CoCoRaHs, GHCN reports Webcams

IMS Capacities

Current Output – 4 & 24km Northern

Hemisphere Analysis

– Snow & Ice Cover

– ASCII, BIN, GeoTiff, Grib2*

– 1x day production

– Little Metadata

Future Output – 1, 4, & 24km Northern Hemisphere

Analysis

– Snow & Ice Cover

– ASCII, BIN, GeoTiff, Grib2

– 2x day production

– Improved MetaData

– Automated 2km Southern Hemisphere Analysis

– Date since last confirmed observation

– Snow Depth (with uncertainty values)

– Sea Ice Thickness (with uncertainty values)

– Same underlying Snow & Ice cover resample algorthrims

Future Capacities (2013)

GOES (E & W)

MeteoSat (MSG (ch 1,2,3) & 7)

MTSAT

NOAA Automated Snow & Ice

AVHRR (Channels 1,2,3)

MODIS (Channels 1,2,7,8)

ASCAT

AMSU (Derived snow, ice, rain)

NIC Marginal Ice Zone

NIC & CIS Ice Charts

Surface Obs (METAR)

NOHRSC SNODAS

AFWA Snow Depth

SSMI/S (Derived snow, ice, rain)

MMAB Sea Ice Cons

AMSR 2 (Sea Ice & SWE)

AMSU MIRS Algorithm (SWE, Sea Ice Con, Snow Grain Size)

VIIRS Snow Cover Fraction

VIIRS Ice Age

VIIRS Imagery (I1, I2, I3, & I5)

RadarSat & Sentinal SAR imagery

US RADAR

COOP & GHCN reports

CMC Snow Depth Analysis

CMC RIPS & GIPS 3D var Ice Analysis

US Navy Arctic Cap (ACNFS) Ice Cons & Thickness

GFS Snow Depth Change (24hrs)

OSCAT

Direct Input

MeteoSat 8 RGB Composite

False color composite, Red: ., Green: R(0.6µm), Blue: inverted T(11µm)

Snow is clearly seen as Yellow due to its high R(0.6µm), low R(1.6µm) and low T(11µm)

Surface Station Report

COOP Station Reports Display in IMS V3 SYNOP Station Reports Display in IMS V3

= Surface report with SD = 0 and SF = 0

= Surface report with SD = 0 but SF > 0

= Surface report with SD > 0

= Surface report with SD = 0 and SF = 0

= Surface report with SD = 0 but SF > 0

= Surface report with SD > 0

VIIRS I1,I2,I3 Composite

False color composite, Red: I1, Green: I2, Blue: I3

Snow is clearly seen as Yellow due to its high I1, I2 and low I3

IMS V3 will also apply I5, NCC, Ice Age, and Snow Fraction. Ice Concentrations will be added at a later date.

OceanSat 2 HH&VV composite Date: May 1st 2012

IMS Analysis

SAR imagery SAR is the only sensor with required

combination of:

- Aerial Coverage (global)

- High Resolution

- All Weather (Arctic & cold regions cloud covered 75-85 % of typical winter season)

Great Lakes Imagery Comparison RADARSAT(top) and MODIS(right) Composite of Northern Lake Michigan and Western portions of Lake Huron.

Date: February 20th 2008 Time: RADARSAT 1945 Z MODIS 1909 Z

© CSA

IMS Data Flow

PrePro SD Pro

1 km Image Sectors 4km Image Sectors 24 km World Images Information Files

GUI Pro

Analyst Pro

Final Pro

Snow Depth

ENVI Product

GeoTIFF Image

GIF Image

GRIB-2 Product

ASCII Product

IMS V3 GUI System

Analyst Products

CMC Snow Depth

Surface Data Hourly Reports

North Hemisphere ASI Product MTSAT Imagery

GOES EAST and WEST Image

MMAB SSM/ISea Ice Product

North America ASI Product SSM/I Products

NOAA 18 & 19 & Metop A AVHRR HRPT Imagery

VIIRS Composite Imagery and Derived Snow/Ice Products

NCEP GFS Snow Depth

North Hemisphere ASI “days since last update”

NOAA-18 & 19 & Metop A AVHRR GAC Composite Imagery

NIC Sea Ice Thickness / Concentrations

Snow Depth Station Report

US Radar Obs.

NAIS Sea Ice Model Output ACNFS Sea Ice Model Output

METEOSAT Image NOHRSC Snow Cover

AMSU rain rate and snow & ice coverage

ASCAT sea ice product

SAR Imagery

AMSR 12km & 6km Ice Concentration INDOEX Imagery

MODIS AQUA&TERRA HRPT Imagery USAF Products

Polar MODIS Imagery

MODIS False Color Imagery

NIC MIZ

GeoTIFF and Shapefile Products

ATMS Sea Ice and Snow OSCAT

Elevation & Land type

AMSU MIRS SWE

ATMS MIRS SWE

AMSR 2 SWE

External Input Data

Intermediate Products

External Output

Processing Unit

Legend

Surface Observations

IMS Snow Depth Retrievals

• Automated Analysis: Optimal Interpolation of microwave + surface observations

• Downscaling based on elevation

• Interactive Analysis: Analyst SD estimate

• Previous analysis as First Guess

Retrieval Examples: IMS+ NASA AMSR-E SD

IMS SD Analysis Day 1

IMS SD Analysis Day 2

Southern Hemisphere Automated Snow & Ice Cover

• Limited areas with snow cover outside of Antarctica

• Southern Hemisphere snow and ice cover will be fully automated (for now)

• Unable to make snow depth, but will work on in future IMS versions.

Direct Import of Automated Snow & Ice Cover

• Analysts will be able to selectively import the data from satellite derived products directly into the IMS analysis

• Analysis will have selection box to import snow cover and ice cover from the VIIRS, NOHRSC, AutoSnowIce (and perhaps MODIS)

• Human data assimilation to optimize product use based on expert knowledge and imagery interpretation

• Combines the reliability of automated products with the QC and flexibility of Human Analysts

• Working towards “semi-automated” SCA and SIC in later versions.

GOES- W GOES- W with AutoSnow overlay

Imported AutoSnow into IMS Analysis

Final Analysis after Analyst edits

Microwave Integrated Retrieval System (MIRS) SWE Products

Composite is based on MiRS currently daily running satellite sensors: N18,N19,Metop-A,F16 SSMIS,F18 SSMIS and NPP ATMS.

Other PreOp NOAA NESDIS Snow Products

AMSU Snowfall detection

AMSR SD/SWE Estimate

• Snow Fraction

• Snowfall from atmospheric sounders

• Snow Depth / SWE from GOES R, ATMS, AMSR 2, and even Blended with IMS Analysis

• Integrated Snow Satellite Products with Snow Models

• Blending NOHRSC / IMS snow depth

• Snow Grain Size

• Sub-grid Snow Depth / SWE Variability

Conclusions

• IMS V3 has many new imagery sources • System Design based on legacy code • Climate Monitoring applications considered for IMS V3 • All data formats more accessible • Return to date since last observed • Sea Ice Thickness output • Novel approach to global 4km Snow Depth, but

validation needed • Blending with automated data • IMS V3 Operational release August 2013 • Many new automated or semi-automated products for

snow are pending release

Questions?

Optimal Interpolation (OI) for Snow Depth

• The analysis starts with a background or first guess Snow Depth (SD) at the analysis grid point, k. This value can be derived from climatology or from analysis at a previous time, e.g., previous day analysis.

• Given the first guess value SD at grid point k, the analysis increment,

at grid point k is defined as:

• Where are the Snow depth increments at observation points.

• The weights w are computed as a set of linear equations from the correlation coefficients between observation points :

n

i

ikik SDwSD1

kSD

)()( ijijij zr

iSD