California Institute of Technology Green Book for Real-Time Weather and Atmospheric Characterization Data Dr. Yoshihisa Takayama Dr. Randall J. Alliss

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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Books to be Developed by OCWG

Blue Book for Optical Communications Physical Layer

Blue Book for Optical Communications

Coding & Synchronization

Green Book for Real-Time Weather and

Atmospheric Characterization Data


• 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?

Objectives of Green Book

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This briefing provides contents of the 1st draft of the Green Book


From CCSDS Optical Communications (OPT) Working Group Concept Paper

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


• Yoshihisa Takayama – NICT writer

• Dimitar Kolev– NICT writer

• Randy Alliss – NASA/NGC writer

• Sabino Piazolla – NASA/JPL writer

• Lena Braatz – NASA/BAH editor

Main Contributing Team Members

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Table of Contents of Green Book

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• 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

Background / Purpose

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• 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

Scope of this book

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• 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

Document Structure

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This briefing provides contents of the 1st draft of the green book for group discussion


BACKGROUND

Slides to be added by Randy / Sabino

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• 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

Background

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Cumulus

BackgroundCloud impacts are main driver for availability

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StratusStratus

Alto Stratus

Alto-cumulusCirrus

Clouds attenuate through absorption (water clouds) and scattering effects (ice crystals)

zenithhorizon


• 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

BackgroundGeographic Diversity mitigates effects

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TMF

Palomar

Flagstaff SOR

WSC

Livermore

Cor

rela

tion

with

TM

F

High Correlation

Low Correlation

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

Derived from GOES cloud database (1995-2013)



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• 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%


• Real-time local characterization of clouds enables intelligent handover decisions.

• Network availability is a function of satellite handover time for a single-head 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.

BackgroundRealtime Handover

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In nearly all cases network availability benefits from local knowledge of cloud cover.


• 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

BackgroundValue of long term statistics…

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• 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


This data has been invaluable in performing system definition studies for FSOC systems (e.g., OLSG)


• 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


1910 km cloud base height 5 km cloud base height 2 km cloud base height


• 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


• 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.


• 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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No Calima (Saharan Dust)1700 UTC July 12, 2007

Severe Calima (Saharan Dust)1700 UTC July 17, 2007

http://www.not.iac.es/weather/index.php?v=webcam1


PHYSICAL QUANTITIES TO BE MEASURED

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• 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

Physical quantities to be measuredRequired measurements


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• 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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• 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

Time

Time

Time

Time

Time

Received powerBeam wander

Scintillation

Combined effect


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• 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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Aerosol/sky radiance measurements (NASA)



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• 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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3.2 Optional measurements (NASA)

3.2.1 Rayleigh scattering

3.2.2 Molecular absorption

Physical quantities to be measuredOptional measurements


INSTRUMENTS

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• 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.

InstrumentsRequired instruments

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• 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.


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All Sky Infrared Visible Analyzer (ASIVA)

NICT infrared WSI


• 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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• 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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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

Sun Photometry is used to study atmospheric transmission and daytime sky radiance at Table Mountain and Goldstone


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• 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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• 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 1 s


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• 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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4.2 Optional instruments (NASA)

4.2.1 Instruments to measure Rayleigh scattering

4.2.2 Instruments to measure Molecular absorption

InstrumentsOptional instruments


REQUIREMENTS FOR REAL-TIME COLLECTION OF PHYSICAL QUANTITIES

Slides to be provided by Randy / Sabino

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• 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

Time Scales for collection

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• 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– This can occur during the early morning even under clear skies


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• 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

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• 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.

Lead time for weather predictions

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• 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).

Lead time for weather predictions

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• 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.

Lead time for weather predictionsDependence on the CONOPS

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• 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:– Nowcast– Persistence– Advection– Numerical Weather Prediction

Lead time for weather predictionsTechnology required for predictions

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• 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)

Lead time for weather predictionsNowcast

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• 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 ~ -25 ~10 100~ -50

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3015

5


• 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

Lead time for weather predictionsPersistence

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Satellite derived persistence at 15 minute intervals:Given clear what is the probability

the site remains clear


• 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

Lead time for weather predictionsAdvection

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The correlation of an advection forecast with Truthas a function of lead time

Workswell

Worksok


• 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

Lead time for weather predictionsNumerical Weather Prediction (NWP)

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• 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


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Example of a Regional Model

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Performs well at simulating the large scale cloud systems


• 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


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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.


• Obtain feedback at face 2 face meeting

• Conduct break out session to discuss specifics

Green BookNext Steps

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Documents

California Institute of Technology Green Book for Real-Time Weather and Atmospheric Characterization Data Dr. Yoshihisa Takayama Dr. Randall J. Alliss