Transcript A Comprehensive Review of Cloud Detection and its

California Institute of Technology
Green Book for Real-Time Weather and
Atmospheric Characterization Data
Dr. Yoshihisa Takayama
Dr. Randall J. Alliss
CCSDS 2014 Fall Meeting, London, UK
November 2014
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California Institute of Technology
Books to be Developed by OCWG
Blue Book for Optical
Communications
Physical Layer
Green Book for RealTime Weather and
Atmospheric
Characterization Data
Blue Book for Optical
Communications Coding
& Synchronization
Objectives of Green Book
California Institute of Technology
• Provide narrative background on atmospherics and why it
is important to accurately characterize them for optical
links through the atmosphere
• Provide content regarding how long term statistics of
atmospherics has been used to choose a network of
geographically diverse ground sites in order to maximize
availability. What is the value of long term stats for
agencies to decide if they want to build optical
communications?
This briefing provides contents of the 1st draft of the Green Book
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From CCSDS Optical Communications (OPT)
Working Group Concept Paper
California Institute of Technology
Green Book for Real-Time Weather and Atmospheric Characterization Data
Title: Real-Time Weather and Atmospheric Characterization Data
Document Type: Green Book
Description of Document: This Green Book will define the physical quantities to be measured at existing and potential
optical ground station sites in support of space-Earth links CFLOS (Cloud-Free Line-Of-Sight) and link budget
calculations.
Contents of the Green Book:

Physical Quantities to be Measured

Material supporting the use of the parameters
o
Long term statistics
o
Real-time measurements
o
Predictive Weather
Book Editor (estimated resources + Agency Volunteering): 4mm + NICT
Expected Contributing Agencies: ESA, NASA, NICT
Expected Monitoring Agencies: JAXA, DLR, CNES
Schedule: Jan 2014 – Dec 2015
Main Contributing Team Members
California Institute of Technology
• Yoshihisa Takayama – NICT writer
• Dimitar Kolev– NICT writer
• Randy Alliss – NASA/NGC writer
• Sabino Piazolla – NASA/JPL writer
• Lena Braatz – NASA/BAH editor
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Table of Contents of Green Book
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Background / Purpose
California Institute of Technology
• Space-to-ground optical communications are affected by the
presence of cloud cover and other atmospheric effects.
• Therefore, it is critical to accurately measure the long-term
characteristics of critical atmospheric parameters for purposes
of site selection.
• Identify and characterize the atmospheric constituents that are
most responsible for transmission losses in optical
communications links
• Identify the types of instruments required to measure long term
stats and support realtime decisions on handover
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Scope of this book
California Institute of Technology
• Provides a detailed description of the critical
atmospheric parameters (e.g., clouds, turbulence,
aerosols) and how they may be measured using
ground-based instrumentation.
• Provides examples of the types of instruments used
• Does not currently recommend any specific
instruments and/or vendors
• Describes the prediction systems that have been
considered by past studies and the resources required
to enable them
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Document Structure
California Institute of Technology
• There are Five main chapters
–
–
–
–
Background – written by NASA
Physical quantities to be measured – written by NICT
Instruments – written by NICT
Requirements for the realtime collection of physical quantities –
written by NASA
– Using the physical quantities to predict future site conditions –
written by NICT
• Green book currently has over 35 supporting
references
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This briefing provides contents of the 1st draft of the
green book for group discussion
California Institute of Technology
BACKGROUND
Slides to be added by Randy / Sabino
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Background
California Institute of Technology
• Over the last several years, a significant amount of work has
been performed to characterize atmospheric effects in support of
free-space optical communications
– Clouds, aerosols, turbulence
• Years of geostationary, multispectral imagery has been gathered
from satellites, providing the basis for a global database of
clouds
• Field campaigns have been conducted by a number of groups to
characterize individual locations with in situ data
• More recent work has been performed regarding the prediction
of future conditions based on current and recent atmospheric
measurements
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Background
Cloud impacts are main driver for availability
California Institute of Technology
Cirrus
Alto-cumulus
Cumulus
Stratus
Stratus
Alto Stratus
horizon
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zenith
Clouds attenuate through absorption (water clouds) and
scattering effects (ice crystals)
Background
Geographic Diversity mitigates effects
California Institute of Technology
• Ground station diversity is one mitigation method
• Find a set of sites that are uncorrelated from each
other to maximize that any one site in network is
cloud free
• Ideally stations are separated by many hundred’s of
kilometers
• Individual stations may NOT be the best cloud free
sites but as a network are uncorrelated
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Background
Geographic Diversity mitigates effects
California Institute of Technology
Derived from GOES cloud database (1995-2013)
Livermore
Flagstaff
Palomar
SOR
WSC
Correlation with TMF
High Correlation
Low Correlation
TMF
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Clouds occur on relatively large scales producing high correlations within a
few hundred km of a site. Correlations drop to near zero at distances
>1000km
Background
Geographic Diversity mitigates effects
California Institute of Technology
• Site selection optimization for a proposed ten-site network with connectivity to
a satellite in L1 orbit.
• These ten sites together produce a network availability of approximately 95%.
• Average station spacing in this example is on the order of 103 km due to the L1
orbit and the desire to minimize the effects of correlated clouds
• The highest effective availability of any one site is 32%
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Background
Realtime Handover
•
Real-time local characterization of
clouds enables intelligent handover
decisions.
•
Network availability is a function of
satellite handover time for a singlehead spaceborne transmitter.
•
The red line shows the network
availability when no cloud data is
available to make handover
decisions.
•
The blue lines show the network
availability when cloud data is
available with varying degrees of
measurement accuracy.
In nearly all cases network availability benefits from
local knowledge of cloud cover.
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Background
Value of long term statistics…
California Institute of Technology
• Free Space Optical Communications (FSOC) requires
a highly available system, analogous to today’s RF
space systems
• Long term collection of atmospherics is invaluable in
estimating the performance of future FSOC systems
• To date, the primary long term data collection has
been performed with Geostationary meteorological
imagery
• Cloud databases have been derived spanning several
decades now
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Background
Value of long term statistics…
California Institute of Technology
• Developed unique and validated 19+ year (1995 –
present) climatology of clouds over CONUS / Hawaii
– 15 minutes, 4km resolution allows for accurate characterization of cloud
correlations and network performance
• International geostationary imagery collected and
archived to support OCONUS studies (2005 – present)
– Cloud climatologies have also been developed for international regions
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This data has been invaluable in performing system definition
studies for FSOC systems (e.g., OLSG)
Background
Value of long term statistics…
•
California Institute of Technology
Advantages of satellite derived cloud databases
– Resolution is approximately 4 km and 15-60 minute temporal
– Long period of record that encompasses seasonal, yearly, and decadal climate variability
– Laser Communications Network Optimization Tool (LNOT) has been used to support
site selection studies
•
Disadvantages of satellite derived cloud databases
– Clouds sensed from Geostationary orbit; not local ; lacks sufficient resolution to truly
resolve the “Line of Sight”
– Resolution may be insufficient for conducting a real mission
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10 km cloud base height
5 km cloud base height
2 km cloud base height
Background
Value of long term statistics…
California Institute of Technology
• The characterization of Optical Turbulence (OT) at a site is
vital to the mitigation of its effects on the optical
communications link.
• The wavefront traveling through the atmosphere is distorted as
it encounters the OT created by inhomogeneities in the
refractive index, degrading signal quality.
• The ability to characterize the OT above a ground station is
vital and can affect decisions on adaptive optics design and
site selection for new locations.
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This makes the collection of OT data invaluable for system
designers and operators of a site
Background
Value of long term statistics…
• To date, long term
collection of OT data has
been limited to a few sites
(Astronomical sites, JPL,
etc.)
• Simulated climatologies of
OT have been conducted
by NASA using Numerical
Weather Prediction models
• Comparisons with DIMM
data show relatively close
agreement
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Local data collection will be useful in real-time systems to
describe the performance of the link.
California Institute of Technology
Background
Value of long term statistics…
California Institute of Technology
• Aerosols may be considered a secondary or even
tertiary impact on a FSOC link budget
• Typical values of fade are << 1dB
• Long term collection of aerosol data has been
conducted under the AERONET program
• Over two dozen sites have been monitoring aerosol
loading for decades
• Aerosols are well behaved and not likely to impact
the optical link
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Background
Value of long term statistics…
No Calima (Saharan Dust)
1700 UTC July 12, 2007
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http://www.not.iac.es/weather/index.php?v=webcam1
California Institute of Technology
Severe Calima (Saharan Dust)
1700 UTC July 17, 2007
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PHYSICAL QUANTITIES TO BE
MEASURED
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Physical quantities to be measured
Required measurements
California Institute of Technology
• Short explanation about weather parameters and their effect on
lasercom links
• Clouds
- Short definition of clouds and their effect on the links – attenuation that
strongly varies with their content (water or ice)
• Cloud coverage
- Used to estimate link reliability since generally clouds are considered as
link obstacles
1.
Clear
0-1/10th covered
2.
Scattered
1/10th – 5/10th covered
3.
Broken
5/10th – 9/10th covered
4.
Overcast
fully covered
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Physical quantities to be measured
Required measurements
California Institute of Technology
• Cloud attenuation
- Critical parameter for lasercom links. Clouds can insert attenuation in very wide
borders according to the cloud thickness and contents (e.g., ice-based clouds add
optical loss of 1 to 8 dB, while water-based clouds can add 10 dB or more).
• Cloud base height
- It is used to define cloud height and describe the lasercom propagation media –
type of clouds and their contents.
- Low clouds (e.g., cumulus, stratus, etc.) consist of water droplets and their bases
are below 2 km
- Mid-level clouds have base between 2 and 6 km (e.g., altocumulus) and are
generally, but not always, water clouds, depending on atmosphere temperature and
other conditions.
- High clouds are those whose base is above 6 km (e.g., cirrus). They can be made
from ice or water, but more often consist of ice.
- There is often more than one cloud layer, which can add extra loss.
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Physical quantities to be measured
Required measurements
California Institute of Technology
• Optical turbulence
- The wavefront in the receiver plane is substantially distorted due to inhomogeneities
in the index of refraction of the air due to variations in the temperature, humidity,
pressure, and CO2 concentration. The overall degradation in image quality due to random
phase aberrations is called seeing.
• Cn2
- Not directly related to real operating systems and not necessary to measure it. Can
be derived from collected data and useful for system performance evaluation. Also, it
provides relationship between the next three parameters.
Transmit power
Received power
Beam wander
Time
Time
Scintillation
Time
Time
Combined effect
Time
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Physical quantities to be measured
Required measurements
California Institute of Technology
• Fried parameter
- As light travels slower in areas with a higher index, the same absolute path length
becomes effectively longer or shorter from an optical standpoint in regions of greater or
lesser n. This leads to random phase aberrations in the wavefront in the receiving plane.
The Fried parameter is a measure of the aperture over which there is approximately one
radian of rms phase aberration.
• Isoplanatic angle
- The region over which the turbulence pattern is the same is called the isoplanatic
patch. The isoplanatic patch is usually defined in terms of isoplanatic angle.
• Greenwood frequency
- Adaptive optics is a technology used to improve the link performance by reducing the
effect of wavefront distortions due to the index of refraction (n) inhomogeneities in the
atmosphere. As winds move these inhomogeneities, or an optical path is slewed through
the atmosphere due to moving transceivers, the distortions induced by the atmosphere will
change over time. Greenwood frequency is the frequency or bandwidth required for
optimal correction with an adaptive optic system.
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Physical quantities to be measured
Required measurements
Aerosol/sky radiance measurements (NASA)
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California Institute of Technology
Physical quantities to be measured
Required measurements
•
•
•
•
•
•
•
California Institute of Technology
Standard meteorological quantities
Temperature is a measure of warmth or
coldness of an object or substance with reference
to some standard value.
Wind is the flow of gases on a large scale. In the
atmosphere wind is caused by differences in the
atmospheric pressure, where the air moves from a
higher to a lower pressure area.
Specific humidity is the ratio of water vapor
to unit mass of dry air in any given volume of the
mixture and usually it is expressed as a ratio of
grams of water vapor per kg of air.
Pressure is the force per unit area extended on a
surface by the weight of the air above that surface
in the atmosphere.
Rain rate is a measure of the intensity of
rainfall. It is measured by calculating the amount
of rain that falls to the Earth surface per unit area
per unit of time.
Solar irradiance is a measure of the
irradiance (power per unit area) produced by the
Sun in the form of electromagnetic radiation.
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Physical quantities to be measured
Optional measurements
3.2 Optional measurements (NASA)
3.2.1 Rayleigh scattering
3.2.2 Molecular absorption
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California Institute of Technology
California Institute of Technology
INSTRUMENTS
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Instruments
Required instruments
California Institute of Technology
• Whole sky imager
- The whole sky imager (WSI) is a passive (non-emissive) system that acquires images
of the sky dome used for assessing and documenting cloud fields and cloud field dynamics.
The received sky images can be used to evaluate the presence, distribution, shape, and
radiance of clouds over the entire sky.
• Visible
- The visible WSI has a fish eye lens with wide field of view (FOV) that focuses the
whole sky image into a CCD camera. To guarantee proper operation under all weather
conditions, a closed module heater and cooling fan are implemented.
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Instruments
Required instruments
California Institute of Technology
• Infrared WSI
- Apart from the visible WSI that uses a CCD camera and fish-eye lens, an IR
(infrared) cloud sensor could also be used for cloud coverage estimation. It consists of
five passive infrared temperature sensors that are pointed in the north, south, east, west
and vertical directions.
All Sky Infrared Visible Analyzer (ASIVA)
NICT infrared WSI
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Instruments
Required instruments
California Institute of Technology
• Ceilometer
- The ceilometer is a device that uses a laser or other light source to determine the
height of a cloud base.
- Optical drum ceilometer
- Laser ceilometer
- In the NICT system, the infrared cloud sensor data is used to measure the sky
radiation temperature. By using the reference -45ºC at 8000 m and measuring the
temperature of the cloud and next to the ceilometer, cloud base height can be calculated.
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Instruments
Required instruments
•
California Institute of Technology
Differential Image Motion Monitor (DIMM)
- A differential image motion monitor (DIMM) is used to measure the Fried parameter.
- At the front of the telescope is installed a mask with two small apertures, covered
with optical prisms. The light from a light source will be refracted by the prisms and two
images are obtained on the receiving CCD camera. The Fried parameter can be found by
calculating the variance of the relative position of each centroid.
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Instruments
Required instruments
California Institute of Technology
Sun Photometry is used to study atmospheric transmission and daytime sky
radiance
at Table Mountain and Goldstone
Sun photometer
(NASA)
•
Sun Photometer scans the sky during the day to measure
direct solar irradiance and sky radiance at a different
angular distance from the Sun
– Measurements are performed over a discrete number of
wavelength channels from UV to Near IR
– Among the direct data outputs of the measurements:
spectral aerosol optical depth, and sky radiance
•
The instrument autonomously tracks the Sun
•
Cloud coverage and rain limit the operation of instrument
– Cloud free data are produced by proper filtering
•
Long term statistics of the atmospheric transmission and
sky radiance can be produced
•
JPL’s sensors belong to the AERONET global network of
sun-photometers
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Instruments
Required instruments
California Institute of Technology
• Meteorological station
• Temperature
- Temperature sensors measure the amount of heat energy that is generated by an object
or system, allowing the detection of any physical change to that temperature.
- Different types of sensors are discussed.
• Wind
- An anemoscope is a device used to show the direction of the wind or to foretell a
change of wind direction or weather. An anemometer is a device used for measuring wind
speed.
- NICT system characteristics are given as example.
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Instruments
Required instruments
California Institute of Technology
• Specific humidity
- The specific humidity SH, can be derived from Relative humidity (RH). RH is the
most commonly referenced measurement as it is related to how humans perceive
temperature. It is measured by hygrometer.
- Hygrometer types are discussed. NICT system example included.
• Pressure
- A pressure sensor measures pressure, typically of gases or liquids. Some pressure
sensors use a force collector to measure strain due to applied force over an area. Such
sensors can be piezoresistive strain gauge, capacitive, electromagnetic, optical, etc. Other
types of pressure sensors are resonant and thermal.
Parameter
Value
Measurement range
500~1100 hPa
Operating temperature
-40~60º C
Accuracy (20 º C)
±0.25 hPa (±0.15 hPa)
Aging stability
±0.10 hPa/year
Response time
1s
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Instruments
Required instruments
California Institute of Technology
• Rain rate
- Rain is measured using a rain gauge, which
gathers and measures the amount of liquid
precipitation over a set period of time. Typically,
there are many limitations for measurements with rain
gauges - e.g., strong wind is an obstacle to collecting
all the drops, some of the drops will stick to the walls
of the gauge resulting in lower estimated values, etc.
• Pyranometer
- A pyranometer is used to measure broadband solar irradiance on a planar surface and
is designed to measure the solar radiation flux density (W/m2) from a field of view of 180
degrees.
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Instruments
Optional instruments
California Institute of Technology
4.2 Optional instruments (NASA)
4.2.1 Instruments to measure Rayleigh scattering
4.2.2 Instruments to measure Molecular absorption
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California Institute of Technology
REQUIREMENTS FOR REALTIME COLLECTION OF
PHYSICAL QUANTITIES
Slides to be provided by Randy / Sabino
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Time Scales for collection
California Institute of Technology
• It will be necessary to perform station handover
during times when sites are transitioning between
cloudy  clear
• Clouds and their derived products (attenuation,
heights, etc.) will generally need to be collected on
time scales of a minute to support handover decisions
– Required when sky is obscured with thin cirrus (meaning pockets of
deep fade cirrus are embedded)
• Aerosol temporal variability is much less than clouds
and can be measured at hourly intervals
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Time Scales for collection
California Institute of Technology
• Standard Meteorological quantities (Wind,
Temperature, pressure, humidity) may be important
for dome closure decisions
• Monitoring of these quantities on scales of a minute
may be desirable some of the time
• Excessive wind may exceed specs on dome forcing a
dome closure
• Condensation occurs when dew point depression is 0
which may force a dome closure
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– This can occur during the early morning even under clear skies
Time Scales for collection
California Institute of Technology
• OT can produce a significant degradation to the
communications link.
• Unlike clouds, OT varies on millisecond to second
time scales.
• Collection at time scales of a second are critical in
order to monitor link performance and explain deep
fades even under clear skies
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USING THE PHYSICAL
QUANTITIES TO PREDICT
FUTURE SITE CONDITIONS
Slides to be provided by Randy
46
Lead time for weather predictions
California Institute of Technology
• Predictive weather for optical communications is
likely to be a critical requirement in order to achieve
the desired high availabilities.
• Station handover, which is the repointing of the space
terminal from station A to B, will rely on local
weather predictions.
• Depending on the system CONOPS, station handover
is accomplished with a “make before break” or a
“break before make” methodology.
47
Lead time for weather predictions
California Institute of Technology
• In a “make before break” CONOPS, there is more
than one space terminal, and a link with a new site is
established before the current one is broken.
• For a “break before make” CONOPS, there is
assumed to be only one space terminal, which must
end communications with one site to establish a link
with a different site.
• The amount of lead time required for weather
predictions will vary with the system CONOPS, and
will be a function of the distance between the space
terminal and the ground (i.e., the range).
48
Lead time for weather predictions
Dependence on the CONOPS
California Institute of Technology
• A predictive weather system will need to forecast whether a
CFLOS exists at the current time and for some amount of time in
the future.
• Minutes
– LEO / GEO CONOPS
• Hours
– A deep space-to-ground scenario may require up to an hour lead time to
predict CFLOS because of the long transit time. For a Mars scenario the
transit time may approach 30 minutes, requiring at least a 30-minute lead
time for the CFLOS prediction.
• Days
– Weather predictions >day may be required to support the scheduling of site
maintenance. e.g., if a site requires routine maintenance, it may be desirable to
schedule that site to be offline during a time when CFLOS is not available.
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Lead time for weather predictions
Technology required for predictions
California Institute of Technology
• Depending on the CONOPS, varying technologies
will be required for atmospheric prediction.
• It is assumed that all CONOPS will require local
instrumentation
• Three main prediction types:
–
–
–
–
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Nowcast
Persistence
Advection
Numerical Weather Prediction
Lead time for weather predictions
Nowcast
California Institute of Technology
• A nowcast evaluates the current state to make a handover decision
• This example shows how the proximity of clouds to the LOS may be used
for the prediction of cloud blockages in the very near term (a few minutes)
•
WSI quality score determined by
the fraction of clouds within two
concentric rings
– Magnitude of WSI quality score is
cloud/clear fraction
– Sign of new score is based on
cloud in inner ring (negative if
cloud is present)
-100 ~ -50 ~ -25 ~10 100
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60
30
15
5
Lead time for weather predictions
Persistence
California Institute of Technology
• The persistence forecast
predicts that whatever is
observed at the current time
will persist for some time
into the future.
• For example, if the whole
sky imager indicates a
CFLOS at time zero then
the persistence forecast says
that a CFLOS will be
maintained indefinitely.
• Persistence may only work
for a few minutes
particularly during p/c
conditions
52
Satellite derived persistence at 15 minute intervals:
Given clear what is the probability
the site remains clear
Lead time for weather predictions
Advection
California Institute of Technology
• A cloud forecast can be derived from the recent
motion of cloud elements, whether they be observed
from a satellite looking down or from the ground
looking up.
• The idea behind the advection forecast is to look for
patterns in motion and assume they will continue over
some period of time.
• May be superior to a persistence forecast but only out
to ~two hours
53
Lead time for weather predictions
Advection
54
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Lead time for weather predictions
Advection
California Institute of Technology
Works
well
Works
ok
The correlation of an advection forecast with Truth
as a function of lead time
55
Lead time for weather predictions
Numerical Weather Prediction (NWP)
California Institute of Technology
• There may be applications that could benefit from longer-lead
time cloud predictions (> day).
– Predictions for site maintenance windows
– Predictions for a network outage (all sites cloudy!) benefiting data
dissemination strategies
• NWP uses mathematical models of the atmosphere and oceans
to predict the weather based on current (initial value problem)
weather conditions.
• Global and regional forecast models are run by different
countries (US, Europe, Japan), using weather observations
relayed from radiosondes (i.e., weather balloons) and
meteorological satellites to describe the initial state in 3D
56
Lead time for weather predictions
Numerical Weather Prediction (NWP)
California Institute of Technology
• Global (regional) models resolve the atmosphere on
scales of 25-50km (<10km)
• Models predict out to two weeks
• A promising new technology is the Ensemble NWP
method
• Ensembles are a basket of models which run with
various physics and initial states so a distribution of
outcomes are generated
– Quantifies uncertainty
57
Example of a Regional Model
California Institute of Technology
Performs well at simulating the large scale cloud systems
58
Lead time for weather predictions
Numerical Weather Prediction (NWP)
California Institute of Technology
• Several models were evaluated
during LLCD
• Regional (SREF) – 12km resolution
• Global (GENS) – 111km resolution
• Regional outperforms Global model
• Correlation with truth decreases
with lead time
Correlations with truth are not great
One area of improvement would be to assimilate cloud data from WSI
into a mesoscale model, which would improve initial conditions and
produce a better quality forecast.
59
Green Book
Next Steps
California Institute of Technology
• Obtain feedback at face 2 face meeting
• Conduct break out session to discuss specifics
60