modis imagery and products in an operational forecasting environment · 2016-07-29 · modis...
TRANSCRIPT
MO
DIS
Im
agery
and
MO
DIS
Im
agery
and
Pro
ducts
in a
n O
pera
tional
Pro
ducts
in a
n O
pera
tional
Fore
casting E
nvironm
ent
Fore
casting E
nvironm
ent
Jordan Joel Gerth
Jordan Joel Gerth
Cooperative Institute for
Cooperative Institute for
Meteorological Satellite Studies
Meteorological Satellite Studies
and
and
Space Science and Engineering Center
Space Science and Engineering Center
University of Wisconsin at Madison
University of Wisconsin at Madison
Wednesday, November 1, 2006
Wednesday, November 1, 2006
Overv
iew
Overv
iew
��Participating Offices
Participating Offices
��AWIPS D
AWIPS D--2D2D
��Types of Im
agery and Products
Types of Im
agery and Products
��Processing and Delivery Mechanism
Processing and Delivery Mechanism
��Hurdles
Hurdles
��Most and Least Used
Most and Least Used
��Strengths and Weaknesses
Strengths and Weaknesses
��Value to Forecaster
Value to Forecaster
Overv
iew
Overv
iew
��There is an ongoing two
There is an ongoing two--pronged effort in
pronged effort in
support of providing MODIS data to the
support of providing MODIS data to the
National Weather Service:
National Weather Service:
••MSFC SPORT project
MSFC SPORT project --Short
Short--term
Prediction
term
Prediction
Research and Transition Center whose goal is
Research and Transition Center whose goal is
to use NASA Earth Science Enterprise (ESE)
to use NASA Earth Science Enterprise (ESE)
observations to improve short term
(0
observations to improve short term
(0--24hr)
24hr)
forecasts. They are using University of
forecasts. They are using University of
Wisconsin
Wisconsin--Madison DB MODIS and AMSR
Madison DB MODIS and AMSR--E E
products for distribution to forecast offices in
products for distribution to forecast offices in
the Southern Region.
the Southern Region.
••UW
UW--Madison
Madison --Supporting NWS MODIS Direct
Supporting NWS MODIS Direct
Broadcast data delivery into AWIPS for the
Broadcast data delivery into AWIPS for the
Central Region forecast offices.
Central Region forecast offices.
Instructions A
vaila
ble
Onlin
eIn
structions A
vaila
ble
Onlin
ehttp://cimss.ssec.wisc.edu/~jordang/awips-m
odis/
Partic
ipating O
ffic
es
Partic
ipating O
ffic
es
Current
Current
��Davenport, Iowa (DVN)
Davenport, Iowa (DVN)
��La Crosse, Wisconsin (ARX)
La Crosse, Wisconsin (ARX)
��Milwaukee/Sullivan, Wisconsin (MKX)
Milwaukee/Sullivan, Wisconsin (MKX)
��Riverton, Wyoming (RIW
)Riverton, Wyoming (RIW
)
Future
Future
��Des Moines, Iowa (DMX)
Des Moines, Iowa (DMX)
��More
More
AW
IPS
DA
WIP
S D
-- 2D
2D
��Advanced Weather Inform
ation
Advanced Weather Inform
ation
Processing System
Processing System
��Display Two
Display Two--Dimensions
Dimensions
��GUI; no command line
GUI; no command line
��One
One--stop mechanism for gathering
stop mechanism for gathering
and viewing all operational weather
and viewing all operational weather
data at National Weather Service
data at National Weather Service
field offices, including model data,
field offices, including model data,
satellite data, observations,
satellite data, observations,
lightning, local radar, etc.
lightning, local radar, etc.
AW
IPS
DA
WIP
S D
-- 2D
2D
Panes
Types o
f Im
agery
and P
roducts
Types o
f Im
agery
and P
roducts
��1 Kilometer Resolution
1 Kilometer Resolution
••Visible (Band 1)
Visible (Band 1)
••Snow/Ice (Band 7)
Snow/Ice (Band 7)
••Cirrus (Band 26)
Cirrus (Band 26)
••3.73.7µµm (Band 20)
m (Band 20)
••Water Vapor (Band 27)
Water Vapor (Band 27)
••IR Window (Band 31)
IR Window (Band 31)
••1111µµm
m ––
3.73.7µµm product
m product
Sam
ple
Im
ages
Sam
ple
Im
ages
3.7µµm Cirrus
Snow
Visible
Sam
ple
Im
ages
Sam
ple
Im
ages
WV
11µµm
m ––
3.73.7µµmm
IR
Types o
f Im
agery
and P
roducts
Types o
f Im
agery
and P
roducts
��4 Kilometer Resolution
4 Kilometer Resolution
••Total
Total Precipitable
PrecipitableWater (TPW)
Water (TPW)
••Cloud Phase (CTP)
Cloud Phase (CTP)
••Cloud Top Temperature (CTT)
Cloud Top Temperature (CTT)
��Marine (1 Kilometer)
Marine (1 Kilometer)
••Sea Surface Temperature (SST)
Sea Surface Temperature (SST)
Two sets: eastern and western
Two sets: eastern and western
United States
United States
Sam
ple
Im
ages
Sam
ple
Im
ages
TPW
SST
(Daytime)
CTT
Pro
cessin
g M
echanis
mP
rocessin
g M
echanis
m
��Obtain a
Obtain a McIDAS
McIDAS(University of
(University of
Wisconsin Visualization Tool) area file
Wisconsin Visualization Tool) area file
of image or product
of image or product
��Fit to a predefined region used in
Fit to a predefined region used in
AWIPS (
AWIPS (eastConus
eastConus, , westConus
westConus))
��Zero
Zero--fill area of
fill area of NetCDF
NetCDFwhere there
where there
is no subset of the MODIS pass
is no subset of the MODIS pass
��Compress using
Compress using zlib
zlib
��Apply naming convention
Apply naming convention
Pro
cessin
g M
echanis
mP
rocessin
g M
echanis
m
SSEC_AWIPS_MODIS_EAST_4KM_CPI_AQUA_
YYYYMMDD_HHMM.7361
Deliv
ery
Pro
cess
Deliv
ery
Pro
cess
LDM
Space Science and
Engineering Center
Madison, WI
LDM
Central Region
Headquarters
Kansas City, MO
LDM on LDAD
NWSFO
Milwaukee, WI
LDM on LDAD
NWSFO
La Crosse, WI
LDM on LDAD
NWSFO
Riverton, WY
LDM on LDAD
NWSFO
Davenport, IA
EXP feed
Quality control machine:
Local AWIPS workstation
96 kbps
connection
Hurd
les
Hurd
les
��Local Data Manager (LDM)
Local Data Manager (LDM)
••Compatibility between LDM5 and LDM6
Compatibility between LDM5 and LDM6
••Size of queue
Size of queue
��Local Data and Dissemination (LDAD)
Local Data and Dissemination (LDAD)
••Receiving machine at NWS field offices
Receiving machine at NWS field offices
is not Linux; slow
is not Linux; slow
��Bandwidth
Bandwidth
��Load time
Load time
••Loops
Loops
LDAD
SBN/
NOAA
PORT
Weaknesses
Weaknesses
��Delayed
Delayed
••Processing and delivery takes over an hour
Processing and delivery takes over an hour
��Working to improve
Working to improve
��Lack of Consistency
Lack of Consistency
••Forecasters have difficulty memorizing Terra
Forecasters have difficulty memorizing Terra
and Aqua pass schedules
and Aqua pass schedules
��Similarity to other satellites
Similarity to other satellites
••Since GOES visible imagery is available in a
Since GOES visible imagery is available in a
timely manner, there is not much benefit to
timely manner, there is not much benefit to
using MODIS visible
using MODIS visible
••Addition of POES in upcoming builds
Addition of POES in upcoming builds
MO
DIS
Im
agery
Usage
MO
DIS
Im
agery
Usage
During forecast
During forecast
preparation:
preparation:
��Most Used
Most Used
••1111µµm
m ––
3.73.7µµm
m
Product (Fog)
Product (Fog)
••Total
Total Precipitable
Precipitable
Water (TPW)
Water (TPW)
••Sea Surface
Sea Surface
Temperature (SST)
Temperature (SST)
••Water Vapor (W
V)
Water Vapor (W
V)
��Least Used
Least Used
••Visible
Visible
••Cirrus
Cirrus
��Growing Use
Growing Use
••Snow/Ice
Snow/Ice
••Cloud Phase
Cloud Phase
MODIS
defines this
band of
clouds better
than GOES.
Should I add
showers for
this afternoon?
Strength
sS
trength
s
��Creates viable connection between
Creates viable connection between
research environment and National
research environment and National
Weather Service field offices
Weather Service field offices
��High resolution, better quality
High resolution, better quality
••Depiction of small
Depiction of small--scale features
scale features
��New products
New products
••Cloud Phase
Cloud Phase
••Sea Surface Temperature
Sea Surface Temperature
��Upwelling
Upwelling
Valu
e to F
ore
caste
rV
alu
e to F
ore
caste
r
��Near
Near --term
(less than 12 hours) forecasts
term
(less than 12 hours) forecasts
••Diagnosing heavy precipitation potential
Diagnosing heavy precipitation potential
��Total
Total Precipitable
PrecipitableWater (TPW)
Water (TPW)
••Determ
ining precipitation type
Determ
ining precipitation type
��Snow or freezing drizzle?
Snow or freezing drizzle?
��Short
Short--term
(12 to 36 hours) forecasts
term
(12 to 36 hours) forecasts
••Areas of fog form
ation
Areas of fog form
ation
••Temperatures in lakeshore areas
Temperatures in lakeshore areas
��Post
Post --event analysis
event analysis
••Temperature of significant
Temperature of significant
convective cells
convective cells
Valu
e to F
ore
caste
rV
alu
e to F
ore
caste
r
��Aviation
Aviation
••Small
Small--scale
scale orographic
orographicturbulence
turbulence
��Climatology
Climatology
••Diagnosing areas of accumulated snow
Diagnosing areas of accumulated snow
••Form
ation of ice on sizeable lakes and
Form
ation of ice on sizeable lakes and
other waterways
other waterways
��Marine
Marine
••Wind shift on Great Lakes
Wind shift on Great Lakes
��Local phenomena
Local phenomena
MAIN SHORT TERM FORECAST PROBLEM IS EAST FLOW AND MARINE LAYER INFLUENCE OVER
EASTERN WISCONSIN...AND DENSE FOG POTENTIAL IN THE WEST. THINK MOST OF THE
DENSE FOG WOULD BE IN THE RIVER VALLEYS...WITH A TENDENCY FOR PATCHY FOG AND
SOME STRATUS AGAIN IN THE EAST WITH MORE OF A GRADIENT. MODIS 1 KM IMAGERY
LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO
THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL
RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)
MAIN SHORT TERM FORECAST PROBLEM IS EAST FLOW AND MARINE LAYER INFLUENCE OVER
EASTERN WISCONSIN...AND DENSE FOG POTENTIAL IN THE WEST. THINK MOST OF THE
DENSE FOG WOULD BE IN THE RIVER VALLEYS...WITH A TENDENCY FOR PATCHY FOG AND
SOME STRATUS AGAIN IN THE EAST WITH MORE OF A GRADIENT. MODIS 1 KM IMAGERY
MODIS 1 KM IMAGERY
LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CO
LAST NIGHT SHOWED THE DENSE FOG IN LONE ROCK AND BOSCOBEL WAS CONFINED TO
NFINED TO
THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. TH
THE IMMEDIATE WISCONSIN RIVER VALLEY...IMPORTANT INFORMATION. THE LOCAL
E LOCAL
RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HEN
RIVER VALLEY DENSE FOG IS NOT SEEN IN THE NORMAL 2 KM GOES. (HENTZ/MKX)
TZ/MKX)
Are
a F
ore
cast D
iscussio
nA
rea F
ore
cast D
iscussio
n
Inte
resting E
xam
ple
sIn
tere
sting E
xam
ple
s
Courtesy of
Courtesy of
Scott
Scott Bachmeier
Bachmeier
(CIMSS/SSEC)
(CIMSS/SSEC)
Futu
re D
evelo
pm
ents
Futu
re D
evelo
pm
ents
��Guided by needs of the forecasters
Guided by needs of the forecasters
••Constrained by bandwidth
Constrained by bandwidth
��True color imagery
True color imagery
••Fixed enhancement of 256 colors
Fixed enhancement of 256 colors
��250 m visible imagery
250 m visible imagery
••Weigh operational significance against
Weigh operational significance against
interesting aspects and size (bandwidth
interesting aspects and size (bandwidth
usage) of the product
usage) of the product
��Norm
alized Difference Vegetation
Norm
alized Difference Vegetation
Index (NDVI)
Index (NDVI)
Conclu
sio
nC
onclu
sio
n
��With the duties of
With the duties of
the forecaster in
the forecaster in
mind, the MODIS
mind, the MODIS
in AWIPS project
in AWIPS project
can be successful
can be successful
••““How can MODIS
How can MODIS
imagery enhance
imagery enhance
the forecasting
the forecasting
process?
process?””
��Questions?
Questions?
Jordan Joel Gerth, CIMSS/SSEC, October 2006