NextGen IOC Sensor Assessment Report

stagetofuAI and Robotics

Oct 29, 2013 (3 years and 8 months ago)


NextGen IOC Sensor Assessment Report

Executive Summary




Context and Motivation


In 2002, the FAA published a Mission Need Statement (MNS #339) for Aviation Weather. This
document addresses “the overall need
for and requirements of aviation weather information
and its delivery to users.” The needed capability is summarized by this qu
ote from the

The aviation weather mission need is to (1) detect and forecast operationally significant en
route and terminal weather events (in real time or near real time) on the surface and aloft that
affect the safety, orderliness, and efficie
ncy of NAS operations and (2) disseminate the
information to the appropriate decision makers.

In 2003 Congress endorsed the NextGen concept in response to the realization that the current
system will not be able to meet growing air traffic demand and that
a concerted effort was
needed to address this problem. Legislation established the Joint Planning and Development
Office (JPDO) to lead the planning for NextGen. The JPDO has published a Concept of
Operations for NextGen as envisioned in 2025.


also developed a set of operational
improvements needed in order to make the 2025 vision a reality. The JPDO has published the
Weather Concept of Operations

which expands on the vision for weather in 2025.

The goal of the Next Generation Air Transportat
ion System (NextGen) is to significantly increase
the safety, security, capacity, efficiency, and environmental compatibility of air transportation
operations by 2025. A cardinal principle is to provide DSTs information designed for specific
NextGen opera
tional decisions. The weather information suitable for such DSTs must be
suitable in terms of content, specification, accuracy, spatial and temporal resolution,
consistency, refresh rate, and availability. They must be usable directly by these tools in th
sense that the formats, and access methods, are machine interpretable
and multiple users
collaborating on decisions can benefit by having the same “Common weather picture.” By
assimilating weather into decision making, weather information becomes an ena
bler for
optimizing NextGen operations

4D weather cube

Under NNEW, the improved weather observations, model outputs, analyses, and forecasts will
reside in a virtual repository known as the 4
D Weather Data Cube, which contains all
unclassified weather in
formation used directly or indirectly to make aviation decisions. The 4
Weather Data Cube does two things. First, it provides complete and efficient access to the
weather information (observations, analyses, and forecasts) required by decision makers in
NAS. Second, it provides complete and efficient access to the observations required by weather


NextGen Concept of Operations v2.0, 13 June 2007


Weather Concept of Operations Version 1.0, May 13, 2006

information producers to make those analyses and forecasts. Selected weather data from the
D Weather Data Cube will be merged and processed to provide a co
nsistent common
weather picture to support ATM decision making known as the 4
D Weather Single
Authoritative Source (SAS)Thus, for a given point in space and time, there will be only one
observation or forecast used for ATM decision making.

The SAS is to
be a key element of the NextGen 4
D Weather Data Cube. The SAS will manage an
integrated, distributed virtual database of local, regional, nationwide and global weather
information from many NAS and non
NAS sources. As a virtual database, the SAS neither
erforms calculations on its data, nor produces derived data products. The SAS will integrate a
variety of observations at various spatial and temporal resolutions into a ‘modeling capability’
that will ‘de
conflict’ overlapping observations and conflictin
g forecasts and provide a baseline
set of gridded weather fields accessible to all Air Traffic Management decision makers for a
‘common weather picture’ across the entire NAS. SAS weather information will ultimately help
to determine safe and efficient fl
ight clearances based on the performance parameters of the
individual aircraft, their pilots proficiency, and the aircraft’s trajectory
based, intended path as
modified to avoid current and developing weather hazards. The SAS weather information

and forecast

for meeting NextGen goals

For RWI, improved weather observational information will be required for NextGen users to
accurately and quickly assess the state of the atmosphere and to support accurate forecasts of
weather impacting NAS operations. The goal is to significantly improve detection of
aviation impact weather (e.g., turbulence and icing) and to right
size sensor configurations and
ground infrastructure. Optimized, externally controllable and configurable
airborne, and satellite atmospheric
sensing networks that provide higher resolution weather
observations will be developed to support more accurate weather forecasts and directly detect
aviation safety hazards; thereby supporting more accurat
e weather forecasts. A focus area will
be to consolidate and replace radars, as well as to improve weather radars technologies.

The second improvement area under RWI, NextGen Weather Forecasting improvements, will
build on the improved observations to pro
vide better analyses and forecasts of aviation
relevant weather phenomena. Improved analysis/forecast information will allow users to safely
plan and conduct 4
D, gate
gate, trajectory
based operations that avoid aviation relevant
hazards and meet oper
ational user business rules. Enhanced weather forecasts will be
developed for icing, turbulence, wind shear, ceiling and visibility, volcanic ash dispersion, and
space weather. The integration of these forecasts into a consistent common weather picture
ll also be used to develop:

The environmental information needed to reduce noise propagation, dispersion of
aerosols, and exhaust impacts

The information regarding the transport and decay of wake vortices needed to
reduce aircraft separation

The space wea
ther forecast information needed to mitigate the harmful effects of
solar radiation on the health of flight crews and passengers while minimizing
impacts on communications, navigation, and other NextGen systems

Improvements will also include new and enhan
ced quality assessment techniques for forecast
product accuracy, weather
specific and higher resolution numerical models to support
diagnostic and probabilistic forecast processes,
as well as establishing and monitoring metrics
to evaluate the progress of

he third improvement area under RWI, weather integration support for decision support, will
utilize the observational, forecast (including probabilities), and network capabilities to provide
decision makers appropriate weather information. The m
anagement of air traffic, especially
within the context of NextGen operations, is very complicated. The widespread use of
computers and sophisticated management tools are required if the goals of NextGen are to be
met. To ensure weather information is an e
nabler for optimizing NextGen operations, weather
information will be translated into information that is directly relevant to NextGen users and
service providers through their DST, such as the likelihood of flight deviation, airspace
permeability, and cap
acity. Rather than a wide variety of human decision makers using a small
number of aviation weather products (i.e. deterministic text and graphics), weather
information (i.e., probabilistic and digital) will be shaped to fit the DST that will optimize NAS
efficiency, manage flight operations, separation management, capacity management, trajectory
management, and airport operations. It will also mean all decision makers and DST will access
the same consistent weather. DSTs will subscribe to data from the 4
D Weather Data Cube to
incorporate weather data and bypass the need for human interpretation, allowing decision
makers to determine the best response to weather’s potential operational effects (both tactical
and strategic) and minimizing the effects of wea
ther on NextGen operations.


RWI Sensor R
Sizing Program Goals

The FAA has identified, in regards to weather data, four improvement areas: observations,
forecasts, integration, and dissemination. Figure 2 depicts the allocation of these improvement
eas to NNEW and RWI. Under RWI, the density and quality of atmospheric weather
observations will be improved by optimizing/developing atmospheric
sensing networks.
Improved weather observations will, in turn, enable improved weather modeling, analysis, a

ment of

sensor network for meeting NextGen weather observation

The first step in the acquisition, processing and dissemination of weather data is the actual
sensing of the state of the atmosphere. This sensing is performed by a broad network of
sensors owned and managed by a wide variety of governmental agencies, bot
h federal and
state. This weather information is used in two basic ways; first as input to forecast systems and
processes which project the future state of the atmosphere based on its current state. The
second usage is the direct dissemination
of weather

data and observed phenomena to
stakeholder like pilots and ATC personnel to

the safety and efficiency of flight

The sensor network encompasses
a multitude of different technologies
, including;




and Polar orb


Radars, primarily NEXRAD and TDWR


Surface sensors







dentification of

gaps based on functional and performance requirements

The functional requirement set formed the basis and framework of the IOC assessment activity.
Because the functional requirements inform the scope of the information required by the


To Improve NAS Performance

JPDO defined weather operational improvements
(OI’s) which reduce weather impact.

Provi de i mproved access to weather
i nformati on by al l users

Improve weather
observati ons

Improve qual i ty of current and forecast
weather i nformati on

Integrate weather l ocati on, severi ty and
i mpact i nformation data i nto operati onal
deci si on maki ng







NexGen architecture, and because they do not provide the hard values that could color the
results of the activity, they were the ideal starting point for the assessment
. Although the
original organization of the functional requirements was not optimal, it was maintained in order
to preserve traceability between the functional requirements and any future sets of
performance requirements. During the assessment activity t
he various teams reviewed the
complete list of performance requirements and selected those requirements that fell within
their areas of expertise.

The portfolio and performance requirements played very little role in the early sensor
assessment activities.

The sensor network’s IOC capabilities were assessed in the areas
described by the functional requirements. The main activity involving the performance
requirements was to provide a mapping of the portfolio requirements to the functional
requirements. T
his activity will also be done for the performance requirements when they
become available. Now that we have a mapping of detailed requirements to the detailed
performance assessments, the process of locating and analyzing gaps can proceed. Once the

between required performance and current performance are identified, we can begin to
plan for the mitigation and elimination of said gaps.

Development of master plan for satisfying NextGen weather observation

year ongoing effort


of t
his Report (FY 2009)

sment of sensor network for IOC

The sensor IOC assessment matrix was hosted in two different environments during the course
of the activity; as an Excel spreadsheet and as a web form whose layout mirrored that of the
Excel document. The matrix was also designed to be compatible with the work done by
NOAA on the NOSA database surveys. The spreadsheet lists all of the 309 functional
requirements identified by the requirements group, and allows the observing
atforms’ characteristics that satisfy those requirements to be recorded and
compared. The recorded characteristics were chosen by the RightSizing team and
consolidated as a list that included parameters such as sensor names, operators, accuracies,
horizontal and vertical coverages. To populate the matrix, each team provided inputs to
each functional requirement for platforms and sensors within their field of expertise. In
addition to the information provided by the teams, certain reference material
s were also

Preliminary identification of observational gaps based on functional requirements


Program Management and Schedule


Timeline and Deliverables


The schedule for the right sizing program for FY09 is shown in figure 2.1. The program
milestones, activities and deliverables are shown on the schedule. Although the timeline was

limited for such a comprehensive review and further shortened by program funding delays, the
goals and the schedule of the project were met, and produced delivera
bles with superior
quality and value.

Figure 2.1 FY09 Schedule

FY10 and beyond
(I will add editorial content to explain the nature of the tasks herein)


Team Organization and Responsibilities

FY09 team
(What I would like here is a brief bio
(a couple of lines) of each team
member, of course stressing their quals relative to the RightSizing Program, I will allow
all of you provide these yourselves, I could just make them up myself, but , well you
can imagine.)


FAA Points of Contact (Pocks)


Victor Passetti


Tammy Farrar


Dino Rovito


Mike Richards


Frank Law


Ernest Sessa


The NCAR Research Applications Laboratory (RAL) has an extensive research

program focused
on aviation (approximately $11M/year supported by FAA,

NASA, NWS, and

In particular, people working

under this program have expertise in the areas of
convective and winter

storms, ceiling and visibility, turbulence, in
flight icing, ground

deicing, and wind shear.

Moreover, significant efforts are
underway that

address data
visualization and dissemination, and integration with

decision support tools.

All these research
and development efforts are

geared toward satisfying requirements for network enabled
weather and

weather forecasting capabilities
for the NextGen.




Team Lead



ceiling &














MIT Lincoln Laboratory has played a key role in the
development of weather radar systems,
particularly with respect to aviation needs. Among these systems are the TDWR and ASR
WSP. It has also developed weather products for the FAA based on other sensors such as the
NEXRAD and Doppler lidar. Lincoln La
boratory is currently working on an advanced radar that
will be capable of performing aircraft and weather surveillance simultaneously, the
Multifunction Phased Array Radar (MPAR). Weather data integration and decision support
systems for aviation is also

a strong focus at the Lab, both at the terminal (ITWS) and national
(CIWS) levels. Sensor network coverage and cost
benefit analyses have been a part of Lincoln

Laboratory’s effort for the FAA as well. All of this expertise and infrastructure will be le
to provide radar and lidar coverage and performance characterization, as well as gap analysis
and mitigation plan, for the RWI Sensor Right Sizing program.





radar / Lidars;
fax 781

OU and IU

Dr. Kelvin






Fred Carr




Dr. Beth






Expanded team membership for FY10 and beyond


ESRL access to MADIS


NSSL access to NMQ


Leveraging and Collaborations/Interactions

NOSA assessment

and database

The NOSA database was used as important source of information through out the sensor IOC
assessment. Many of the sensor performance parameters were chosen to closely reflect those
parameters included in NOSA platform surveys. NOSA survey in
formation was included for

each NOSA parameter name that matched or closely matched the phenomena reference in the
functional requirements. This information was included in the spreadsheet and properly
attributed to NOSA as the source of the data. The da
ta can then be assessed and compared to
the information provided by the right sizing team

During 2002 Vice Admiral Conrad C. Lautenbacher, Jr., USN (Ret.) called for a fundamental
review of NOAA's strengths and opportunities for improvement. A Program Revi
ew Team
reviewed and debated issues and developed suggestions for building a better NOAA. These
suggestions led to 68 specific recommendations.

Recommendation 32 addressed centrally planning and integrating NOAA observing systems and
indicated a clear need for a NOAA
wide observing system architecture. The NOAA
Administrator responded:

I concur with the PRT recommendation that NOAA centrally plan

and integrate all observing
systems. I will assign this responsibility to a matrix management team, with NESDIS providing
the program manager. I do not currently endorse the PRT recommendation to assign acquisition
authority for all observing systems to N

NESDIS should lead a cross
cut team to develop an observational architecture commencing
immediately. This should capitalize on on
going efforts (e.g., coastal observations). This
architecture should capture the state today as well as the future sta
te (e.g., 10 to 20 years).
With this architecture, NOAA would be able to assess current capabilities and identify short
term actions.

A cross
cutting team led by NESDIS should conduct a systemic review of all other observing
systems. The following factors

should be considered for observing systems to determine the
desirability of consolidating them:

The required characteristics of the system (i.e., reliability, performance,

The number of and types of users of the system

The estimated va
lue of the capital asset and its recurring maintenance cost

NOAA can manage its observation system more efficiently and effectively with architecture that
defines a consistent set of principles, policies, and standards. The NOAA Observing System
ure (NOSA) Action Group, directed by the NOSA Senior Steering Group, was
established to develop an observational architecture that helps NOAA:

design observing systems that support NOAA's mission and provide maximum value,

avoid duplication of existing s
ystems, and

operate efficiently and in a cost
effective manner.

NOSA includes:

NOAA's observing systems (and others) required to support NOAA's mission,

The relationship among observing systems including how they contribute to support
NOAA's mission and

associated observing requirements, and

the guidelines governing the design of a target architecture and the evolution
toward this target architecture

The RightSizing Sensor Assessment activity has made extensive use of this valuable resource in
ng the programs deliverables especially the Sensor IOC Spreadsheet. Queries were
developed that retrieved relevant information from the NOSA database for many of the
Functional Performance Requirements and returned it in a format that facilitated ingest i
the RightSizing tools. All in all several hundred entries were made utilizing information from
NOSA. The quality of the information was generally quite good, although there were some
instances and data fields where the information returned was anomal
ous. Our general
approach in these cases was to leave the entries intact as they were returned. In questionable
cases additional entries were created correcting or clarifying the fields in question.

NSF Facilities Assessment Database

The National Science

Foundation (NSF) Lower Atmospheric Observing Facilities Program Office
and the National Center for Atmospheric Research (NCAR) Earth Observing Laboratory (EOL) are
conducting a community
wide assessment of atmospheric science related instrumentation. This

assessment is considering facilities across government agencies, universities, national
laboratories, international organizations and private companies. This assessment is considering
a wide breadth of technologies including currently available instrument
ation as well as systems
under development. This database is currently being configured from the collection of
information on observing facilities, platforms, and instruments.

This tool is focused on the performance and operating details of a wide range of

mesonet type
systems spanning the entire US. Since this information is organized on a network performance
basis rather than a sensor performance basis, information from this tool was not incorporated
into the initial incarnation of the Sensor Assessment
Deliverable. Moving forward it is
anticipated that this tool will provide valuable information in terms of determining geospatial
parameter coverages and in provided insights into Gap Filling strategies.

NRC “Network of Networks” report

The Committee envi
sions a distributed adaptive “network of networks” serving multiple
environmental applications near the Earth’s surface. Jointly provided and used by government,
industry, and the public, such observations are essential to enable the vital services and
ilities associated with health, safety, and the economic well
being of our nation.

In considering its vision, practical considerations weighed heavily on the Committee’s
deliberations and in the formulation of its recommendations. To that end, the study
phasizes societal applications and related factors influencing the implementation of an
enhanced observing system, the intent of which is to markedly improve weather
services and decision making. The Committee considered the various roles to be pla
yed by
federal, state, and local governments, and by commercial entities. In essence, the study
provides a framework and recommendations to engage the full range of

for weather,
climate, and related environmentally sensitive information, while en

of this
information to employ an integrated national observation network effectively and efficiently in
their specific applications.

This study does not attempt to compile an exhaustive catalogue of mesoscale observational
assets, although it
identifies and summarizes numerous important sources for such
information. Nor does this study attempt to design a national network, although it does identify
critical system attributes and the ingredients deemed essential to retain sustained importance
d relevance to users.

The RightSizing team remained cognizant of AMS’s activities in support of this report and seeing
its recommendations come to fruition. The entire team attended the AMS summer meeting in
Norman, OK in support of these efforts. It is
anticipated that future RightSizing effort’s goals
will remain compatible and synergistic with the goals of the report.


The Office of the Federal Coordinator for Meteorological Services and Supporting Research,
more briefly known as the Office of the
Federal Coordinator for Meteorology (OFCM), is an
interdepartmental office established because Congress and the Executive Office of the
President recognized the importance of full coordination of federal meteorological activities.
The Department of Commerc
e formed the OFCM in 1964 in response to Public Law 87
Samuel P. Williamson is the Federal Coordinator. Their mission is to ensure the effective use of
federal meteorological resources by leading the systematic coordination of operational weather
uirements and services, and supporting research, among the federal agencies. Victor
Passetti and Thomas Carty are members of the OFCM and have briefed the activities, plans and
intent of the RightSizing Program and the Sensor Assessment initiative to the
group on multiple


I am not sure what to say about this one.

Broader community efforts (e.g., MADIS, Clarus, etc)

One of the first tasks of the RightSizing program was to establish a connection to the MADIS
data distribution network and to ma
ke this data available for study. The MADIS networks
represent a stable, reliable and quality checked source of a wide variety of nontraditional data
sources and systems. The RightSizing program will leverage this data source to study the value
of incorp
orating these types of data networks in the NexGen environment.

The Meteorological Assimilation Data Ingest System (MADIS) is dedicated toward making value
added data available from the National Oceanic and Atmospheric Administration's (NOAA)
Earth System
Research Laboratory (ESRL) Global Systems Division (GSD) (formerly the Forecast
Systems Laboratory (FSL)) for the purpose of improving weather forecasting, by providing
support for data assimilation, numerical weather prediction, and other hydro meteorolog

MADIS subscribers have access to an integrated, reliable, and easy
use database containing
the real
time and archived observational datasets described below. Also available are real
gridded surface analyses that assimilate all of

the MADIS surface datasets (including the highly
dense integrated mesonet data). The grids are produced by the Rapid Update Cycle (RUC)
Surface Assimilation System (RSAS) that runs at ESRL/GSD, which incorporates a 15
km grid

stretching from Alaska in the

north to Central America in the south, and also covers significant
oceanic areas. The RSAS grids are valid at the top of each hour, and are updated every 15

The ESRL/GSD database is available via ftp, by using
Unidata's Local Data Manager (LDM)

software, through the use of

Quality Control (QC) of MADIS observations is also provided, since considerable evidence exists
that the retention of erroneous data, or the rejection of too many good da
ta, can substantially
distort forecast products. Observations in the ESRL/GSD database are stored with a series of
flags indicating the quality of the observation from a variety of perspectives (e.g. temporal
consistency and spatial consistency), or more p
recisely, a series of flags indicating the results of
various QC checks. Users of MADIS can then inspect the flags and decide whether or not to
ingest the observation.

MADIS also includes an Application Program Interface (API) that provides users with easy

to the observations and quality control information. The API allows each user to specify station
and observation types, as well as QC choices, and domain and time boundaries. Many of the
implementation details that arise in data ingest programs are

automatically performed. Users
of the MADIS API, for example, can choose to have their wind data automatically rotated to a
specified grid projection, and/or choose to have mandatory and significant levels from
radiosonde data interleaved, sorted by desce
nding pressure, and corrected for hydrostatic
consistency. The API is designed so that the underlying format of the database is completely
invisible to the user, a design that also allows it to be easily extended to non

The RightSizing
effort also included coordination with the DOT’s Clarus Imitative. The Clarus
program manager briefed the team on the Initiatives progress and capabilities, and a
connection was established with some of the Initiative’s data sources.

The U.S. Department o
f Transportation (DOT) Federal Highway Administration (FHWA) Road
Weather Management Program, in conjunction with the Intelligent Transportation Systems
(ITS) Joint Program Office established the

Initiative in 2004 to reduce the impact of
adverse we
ather conditions on surface transportation users.


is a research and development initiative to demonstrate and evaluate the value of
“Anytime, Anywhere Road Weather Information” that is provided by both public agencies and
the private weather enterp
rise to the breadth of transportation users and operators. The goal
of the initiative is to create a robust data assimilation, quality checking, and data dissemination
system that can provide near real
time atmospheric and pavement observations from the
llective state's investments in road weather information system, environmental sensor
stations (ESS) as well as mobile observations from Automated Vehicle Location (AVL) equipped
trucks and eventually passenger vehicles equipped with transceivers that will

participate in the
Vehicle Infrastructure Integration (VII) Initiative.


Scope and Methodology

The broad aim of this study was to characterize the state of weather sensors at NextGen Initial
Operational Capability (IOC) and to assess
their aggregate capability to meet the 4D Weather
Cube observation requirements. Given the limited time available, however, we decided to
narrow the focus so that specific and useful findings could be reported. This chapter discusses
the methodology that

we used, the motivations for going down this path, and its associated
assumptions and limitations.



Although this study is an assessment of sensors, its
raison d’être

is the weather information
needs of NextGen. Therefore, rather than beginning

with a catalog of sensors and their
characteristics, we took the NextGen 4D Weather Cube observation (i.e., functional)
requirements as the starting point and matched the sensors to each defined entry. However,
the performance requirements were not yet a
vailable during this study, so the sensor mapping
was limited to the functional requirements (JPDO 2008). To be specific, these are all the
requirements that fall under the function “Observe Atmospheric and Space Conditions.” (Note
that these requirement
s cover all of the 4D Weather Cube, not just the smaller subset defined
by the 4D Weather Single Authoritative Source (SAS).) The following hierarchical relation puts
this function into the context of NextGen Enterprise Architecture (EA): F0 NextGen Servi
ces →
F1 Enterprise Services → F1.1 Provide Weather Services → F1.1.1 Observe Atmospheric and
Space Conditions. Sensor assessment relative to the performance requirements is still needed
and will be conducted in FY2010.

To begin, we needed to create a m
anageable structure for the sensor assessment data. We
chose to do this in a spreadsheet, and listed the 311 functional requirements down a column.
Any sensor that would provide data to meet a requirement would be inserted as a row under
the requirement
entry. The columns to the right were used to list the various sensor
characteristics and other associated information. The column labels are listed below.










Measurement System/Platform


Operational Readiness
Status and Timeline


Environmental Parameter Name


NOAA GCMD Variable


Measurement or Derived


Measurement Algorithm


Measurement Units


Measurement Min


Measurement Max


Representative Measurement Accuracy


Representative Measurement Accuracy Units


Measurement Precision


Representative Measurement Precision Units


Representative Measurement Uncertainty


Representative Measurement Uncertainty Units


Data Latency


Environmental Parameter Timeline


Environmental Parameter Timeline Units


Reporting Frequency


mpling Frequency


Sampling Duration


Measurement Stability


Measurement Extent


Other Key Parameter Properties


Remote Sensing (Yes/No)




Geographic Coverage


Geographic Coverage Description


Horizontal Grid Spacing Units


Representative Horizontal
Grid Spacing


Vertical Resolution Units


Representative Vertical Resolution


Associated Spectral Characteristics


Coverage in a GIS Formatted Geospatial Database


Geographical Coverage Data


Coverage Description Web Page


Coverage Description Material

[Is this li
st necessary? If so, should there be an explanation of each entry? Should they go in an

As described in Chapter 2, multiple sub
teams (with multiple members each) contributed to
gathering the sensor assessment data. Thus, in order to
facilitate simultaneous entry by
different individuals, the spreadsheet matrix was translated into a Web
based interactive form
that updated the master database in real time. A separate page was provided for listing the
references cited under the “Source”


In addition to individually filling in this assessment matrix, team members held biweekly
teleconferences and gathered for a three
day workshop to exchange information and ideas,
and to formulate plans. Colleagues not included on the teams were a
lso consulted when their
expertise was needed on a given point. Also, as discussed in Chapter 2, other organizations
have produced or are producing catalogs of weather sensors off of which we could leverage. In
particular, we made extensive use of the NO
AA Observation System Architecture (NOSA)
database in filling out our assessment matrix.

[Insert description of gap identification process?]


Priorities and Limitations

A comprehensive coverage of the large assessment matrix (and subsequent analysis and
discussion) given our resource constraints was not possible. Therefore, we established a set of
priorities to guide us on which areas to focus first. We now discuss these prioritization schemes
by category.

Sensor and data ownership and access

sensors are owned and operated by public and private entities. The public sector is
composed of government organizations at all levels

federal, state, county, city, etc. The
private sector is also diverse, including groups such as universities, televisio
n stations, power
utility companies, etc. The data produced by these sensors can be categorized as open, closed,
or restricted, but the categorization is not necessarily the same as the sensor ownership status.
For example, a public entity (the military)

can keep its data closed (classified), whereas a private
organization (a university) could make its data open to the public. Data access can further be
parsed according to cost (free vs. priced), latency (real
time vs. delayed/archived), format

vs. proprietary), etc.

With regard to our study, federally owned sensors with open
access data garnered top priority.
invested sensors received a lot of attention, since one of the purposes of this study was to
issue recommendations on future decis
ion points in the EA Weather Sensor Roadmap.
Privately owned sensors were also considered if their data status was open access. Sensors
that were not part of a network tended not to be included. Given the time limitations, and
based on the low probabili
ty that they would be available for NextGen use at IOC, we did not
include sensors with closed
access data. Relevant foreign sensor data (such as from the
Canadian weather radars) were not excluded from consideration, but were given low priority.

Sensors and their products

Although the assessment matrix is an attempt to match sensors to weather observation
requirements, in most cases useful weather information (the product) is not obtained directly
from the raw data output of the sensor. Usually,
the raw data is processed further within what
is defined to be the sensor’s own hardware and/or outside it. In some cases the processing
incorporates data from other sensors (of the same kind, different kinds, or both). In other
cases the processing comb
ines numerical model output data with the sensor data to generate
the weather product. Therefore, an entry in the assessment matrix is usually a specific sensor
product rather than the sensor itself.

However, since this study was a sensor assessment, we

prioritized the inclusion of single
products. Multi
sensor products and products incorporating model data were included if there
was a possibility of a functional gap without them.

Sensor status

Sensors (and their data products) can be in vario
us stages of technological maturity. Some
sensors have been in operational mode for many years, while others are still considered
research projects. The emphasis was on systems that are currently operational. However,

since the assessment was for a futu
re time (IOC

2013), we also considered sensors and
products that were expected to be ready for operational use by then. Discussion of even more
experimental systems and processing algorithms were included if there was a possibility of a
functional gap wit
hout them.

[should we be concerned with maintenance and replacement schedules etc. also?]

Coverage and performance

Because the official weather sensor performance requirements were not available during this
study, we prioritized the information gathering

for the sensor assessment matrix to emphasize
functional parameters over specific performance metrics. If left unpopulated this year, the
matrix entries relating to performance, such as measurement accuracy and uncertainty, will be
filled in for next yea
r’s gap analysis that includes performance considerations.

Aviation hazards

Of the long list of weather observation requirements, we focused most intensely on ones that
covered aviation hazards, i.e. phenomena that could lead to loss of lives, injury, ai
rcraft loss or

Coverage domains

Apropos of the above, we prioritized the analysis of coverage in terminal airspace, as that is the
domain most dangerous for flights. Coverage of the other airspace volumes (en route, global)
was also examined, bu
t less attention was focused on them.

The priorities discussed above implicitly point out some of the limitations of our study. As
mentioned already, we assessed the sensor products relative to the functional requirements
and not the performance require
ments. The lower priority (relative to aviation hazards)
requirements were not thoroughly covered, and sensors (and their products) still in the
research and development stage were not characterized fully. Sensors with restricted data
access tended not t
o be included. This study was clearly an initial cut at an assessment that is
an ongoing effort, for which we plan to expand the scope to include many of these areas.


Terminology and Ambiguities

In this section we define the terminology used in this rep
ort that members of the study team
believed could be a source of confusion to the readers. We also discuss some ambiguities that
we encountered in dealing with the functional requirements.

As a way of categorizing the types of sensors, one of the distin
ctions we used was

. This division is fairly self
evident, with the criterion having to do with
whether the weight of the sensor is resting on the surface of the planet or being supported in
the atmosphere above it. There are a f
ew cases that may not seem to be so clear
cut, such as
based and satellite sensors. For the purposes of this report, the former is ground
and the latter is airborne. Sensors on a tethered balloon or kite are considered airborne by our
on, since their weight does not rest on the ground. Sensors mounted on ground
vehicles are considered to be ground based.

Another binary division we employed was
in situ

remote sensing
. This characterization
depends on the distance between th
e sensor and the physical entity from which it obtains
information. A device is in situ if what it observes is either in contact with the sensing element
or is within the physical volume of the sensor. An instrument employs remote sensing if the
entity f
rom which information is obtained is some distance away from the sensor. There are
some potentially ambiguous cases such as the ultrasonic anemometer, where local information
is obtained not by direct contact but through sound emission and receiving, but
we include
such cases under in situ, since the measurement is made only within the immediate vicinity of
the sensor.

In general, an in situ sensor provides a
point observation
, while a remote sensing device yields
volume observation
. However, the term

“point” is not used in the mathematical sense of
possessing no volume. In actuality, a point measurement has a zone of high correlation around
it, and this spatial extent should be taken into account when determining the coverage of an in
situ sensor. F
urthermore, an in situ sensor situated on a moving platform will trace out a line
over the course of a sampling period, so it is not a point observation even in the loose sense.

The classification of the spatial domains used in this report follows the sc
heme outlined in the
preliminary portfolio requirements document (Moy 2008). For above
surface observations,


is the volume of airspace within 100 km of airport centerfield from the
ground up to the top of the terminal volume
]. [Does this apply to all airports or just
OEP or…?]

En route


is the volume of non
oceanic national airspace system (NAS) not
occupied by terminal airspace.


is the union of oceanic and non
NAS airspace.
For surface observation
s, the
terminal area

refers to certain designated areas at

En route area

covers the NAS surface areas minus the terminal areas.
Global area
surface areas outside the NAS.



are often used as complementary te
rms to characterize the
measurement performance of a sensor. However, according to the International Organization
for Standardization (ISO), both are qualitative terms and have multiple definitions (ISO 1993).
Thus, the use of accuracy and precision shou
ld be avoided in expressing quantitative
parameters. Instead we opt to quantify the
, a parameter that characterizes the
range of values in which the measured value lies within a specified confidence level.

We should also clarify the differen
ce between

reporting quantization
. The
former has real physical significance, while the latter is only the fineness of scale at which
measured or derived results are reported or displayed. In the spatial domain, reporting
quantization may
be called grid spacing, gate interval, pixel size, etc. In the temporal domain, it
may be referred to with terms such as reporting interval, output frequency, sample spacing,
etc. These quantities should not be confused with the resolution, which defines

the range
within which the measurement is valid and independent of the neighboring measurements. It
is possible for resolution and reporting quantization to have the same value, but in general they
do not. If the reporting quantization interval is small
er than the resolution interval, the results
are oversampled; if the reverse is true, then the results are undersampled.
[I still don’t
understand why resolution is only applied to the vertical dimension and grid spacing is only
applied to the horizontal
dimension in the spreadsheet.]

As discussed in Section 3.2 regarding sensors and their products, various levels of processing
are applied to raw sensor data to generate weather products. If a sensor product is directly
related to the sensor measurement,

it is classified as
. Otherwise, the product is

[Insert discussion of atmospheric phenomenon vs. parameter/quantity]

As one of the main goals of this sensor assessment is to identify gaps in meeting the weather
observation requirements, we need to discuss what we mean by a gap. At the most basic level,
there could be a
theory gap
, where there is not enough understanding

on how to make
measurements (or even what measurements to make) to meet an observation requirement.
Given the appropriate knowledge, there could still be an

, where the
technology necessary for building the needed sensor (and/or sensor platform) does not exist
yet. If the sensor is built and deployed for research, time and effort are still needed to bring it
to robust operational status; in the mean time, the
re is an
operational gap
. For a derived
product, there will be a
product gap

until an algorithm for generating it is developed,
implemented, and validated.

[The classification and terminology of these gap types are certainly open for debate.]

With the a
vailability of a sensor product capable of fulfilling a functional requirement, there are
still other types of potential gaps. If the spatial domain over which the requirement is defined
is not completely covered, then there is a
spatial coverage gap
. If

the required time coverage
(e.g. 24/7) cannot be met, then there is a
temporal coverage gap
. If any of the performance
requirements are not met, then there is a
performance gap
. There may be a

if access to the sensor product is restric
ted or if the data transfer infrastructure is
inadequate, resulting in missed and/or tardy data. And in the context of the NextGen Network
Enabled Weather (NNEW) program and the network
networks vision, a
metadata gap

hinder the proper characteriza
tion, dissemination, and usage of the sensor product. A

could occur temporarily due to sensor failure, network or power interruption, sabotage,
natural disaster, etc. Finally, any of these gaps can be directly or indirectly produced by a
ing gap

Although the different gap parameters exist independently, they still need to be examined
within the context of each other. For example, performance parameters are often dependent
on the coverage domain. Therefore, in such a case, a gap should

be defined jointly with respect
to both spatial coverage and performance parameters.


IOC Assessment and Key Findings

This chapter summarizes key findings from the IOC assessment. Critical sensors and platforms
are identified, and r
isks to them in the IOC timeframe are pointed out (section 4.1). Projected
gaps with respect to the functional requirements are enumerated and analyzed (section 4.2).
We discuss the important lessons learned in this study (section 4.5), and opportunities

on activities are highlighted (sections 4.3 and 4.4).


Sensor catalogue

The assessment spreadsheet provides a catalogue of sensors that are currently in use or have
the potential to contribute relevant information to be utilized by aviation
users. This sensor list
hasn’t been analyzed and ordered in terms of relevance and priority for aviation users yet. This
remains to be done as part of the FY10 in
depth gap analysis.

Clearly, the sensor catalogue has its shortcomings. The spreadsheet
has grown unwieldy, which
reduces its effectiveness to retrieve relevant information. Some capability to slice and dice the
information in different ways (e.g., by sensor type; by aviation weather hazard; by geographic
location; etc.) would be highly usef
ul. Moreover, flexible search and visualization capabilities
might be envisioned; however, it is apparent that a substantial amount of work would be
required to amend the spreadsheet and its underlying database to enable that. For example,
the sensor lis
t may include a specific type of sensor, but in order to be able to visualize it
geographically one needs metadata for all those sensors individually (which may be hundreds
across the country), including their location and other relevant information.

ther limitation of the spreadsheet is that it builds upon the functional requirements only
without having performance requirements associated with it. The latter have not been
available to the assessment team thus far. Matching up functional and performa
requirements will be a key effort for FY10 and provide the basis for more in
depth gap analyses.
For example, there may be an existing detection capability that satisfies a functional
requirement; however, the performance requirements might not be ach
ievable at present,
which leaves a gap.

Hereafter, some of the most critical platforms and their perceived risk for IOC and beyond are
addressed briefly.

based weather observing systems

[insert some statements about ASOS and LWE etc.]


detection systems

The terminal
area wind
shear sensing requirements are some of the most critical observational
tasks within the NAS. Microbursts along the paths of approach, landing, and departure are the
deadliest weather phenomenon for aviation. With

this in mind, we summarize the expected
state of terminal wind
shear sensors in the future.

The Terminal Doppler Weather Radar (TDWR) is the most capable (and most costly) wind
detection system. It first became operational in May 1994, was fully
deployed by January 2003
and expected to be decommissioned by 2012. However, a service life extension program (SLEP)
was approved and is currently ongoing (anticipated to be done by the beginning of 2013), which
will push off the end of its life to about
2019. The SLEP has now progressed to the point where
it is reasonable to expect that the TDWR will be operational well beyond IOC, so the risk is small
for the TDWR at IOC.

Another radar
based wind
shear detector is the Weather Systems Processor (WSP), which is a
processing system piggybacked onto the Airport Surveillance Radar
9 (ASR
9). In this particular
case, the lifetime depends on both systems. The ASR
9 (initially op
erational in May 1989 and

fully deployed in September 2000) is expected to go completely out of service by the end of
2025, so it will be safe for IOC. The WSP, originally slated for end of service by 2011, is
undergoing a technology refresh (TR) that wil
l extend its life to 2017. The TR is in the
deployment stage, so the WSP also appears to be in good shape for IOC.

As for the anemometer
based wind
shear detection systems, the Low
Level Wind
shear Alert
System (LLWAS) 2, which would have gone out of se
rvice by 2014, will be upgraded through the
relocation and sustainment (RS) program (to be completed by the end of 2012). The new
RS system will then be scheduled for a 2019 decommissioning date. Another version of

the Network Expansion and s
oftware rehost (NE++), itself an upgrade

is slated to be
operational through 2018. The LLWAS systems appear to carry low risk for IOC.

Finally, a Doppler lidar has been installed at the Las Vegas airport to supplement coverage by
the TDWR in areas of ex
treme road clutter, a problem made more intractable for the radar due
to the presence of low
reflectivity dry microbursts there. The lidar is expected to become
operational by the end of 2010. The hardware is a commercial off
shelf product. At the
resent, there are no plans to deploy this system at other locations.

Beyond IOC, the fate of these FAA
owned wind
shear sensors is unclear. The EA Weather
Roadmap calls for investment decisions regarding further SLEP and TR for these sensors in 2010
itial) and 2012 (final). An even bigger decision point looms in 2016, with a wider range of
options such as the replacement of terminal wind
shear detectors and all weather surveillance
radars (including NEXRAD) with a multifunction phased array radar (MP

Airborne wind
shear detectors, operating on the data provided by the weather radar in the
aircraft’s nose cone, are an important supplement to the ground
based systems. These so
called predictive wind shear (PWS) radars, however, are not capable en
ough to replace their
bound counterparts (Hallowell et al. 2009). The equipage rate of commercial aircraft
with PWS radars has increased over time (up to 67% in September 2007, Hallowell et al. 2009).
It is only expected to grow in the future, alth
ough regional jets (and certainly most general
aviation aircraft) may not have enough real estate up front for an effective PWS radar.

Weather radar systems

The NEXRAD is, overall, one of the most indispensable weather observation systems. Its
on of spatiotemporal resolution and en route domain coverage is unmatched by any
other sensor network or satellite instruments. As with many of the other radars, the NEXRAD is
currently undergoing an upgrade. The initial phase of transforming the signal
processing and
product generation platforms into open systems has been completed, and now the dual
polarization hardware renovation is ongoing. The current schedule calls for the dual
polarization system to be deployed nationwide by September 2012. This
is clearly a risk for
IOC, especially since the operational implementation of software builds that incorporate dual
polarization product algorithms will likely lag the hardware schedule. There are observational
requirements that depend on the availability

of dual
polarization radar products (mostly having
to do with hydrometeor classification), which, therefore, may not be met at IOC. Beyond IOC,
NEXRAD is also subject to the EA Weather Roadmap decision point in 2016, when replacement
by MPAR will be cons

Satellite weather observing systems

[insert some statements about satellite sensors]

Key platforms for the space weather requirements include ACE, GOES, and SOHO. On the ACE
satellite the EPAM, MAG, SIS, and SWEPAMA instruments that compose the
Time Solar
Wind (RTSW) data stream are critical. These data are used to predict and monitor geomagnetic
storms activity. The radiation and geomagnetic field sensors on the GOES satellites are critical.
These sensors play a key role in monitoring ra
diation levels especially for solar radiation
monitoring. The LASCO coronagraph on SOHO is crucial for observing solar flares and coronal
mass ejections. It is important to note that a fraction, including ACE and SOHO, of the sensors
identified in this c
atalog are operated by NASA as scientific missions and as such are not
guaranteed to be operation at IOC or beyond.



A wide array of potential gaps may exist, as defined in section 3.3. An in
depth gap analysis will
be performed in FY10. Some obvi
ous gaps based on a preliminary analysis of the functional
requirements are compiled in three tables, listing gaps associated with ground
based sensors
), radar/lidar sensors (Table
), and airborne/spaceborne sensors (Table
). These

provide a “high glance value” and, therefore, we decided not to repeat the respective
contents here in text form also. Instead, the comments below are meant to take a look at types
of gaps from a higher perspective.

Human observations

Current aviation
operations include a significant amount of human
based, visual observations.
These include, for example, dust/sand swirls or storms; funnel clouds and waterspouts; blowing
spray, snow, sand and widespread dust in terminal area; airport, tower and runway v
and biological hazards such as birds; among many others. For most of these phenomena there
exists little or no automated observing capability, and this situation will likely hold for IOC.

Data access and utilization

There are a lot of potenti
ally useful data out there, but they may not be available in real time
(e.g., because of restrictions or communication bandwidth) or have limited data standards and
quality (e.g., TAMDAR).
[list some examples]

Thus, one needs to facilitate better and mor
timely access to existing platforms and sensors, data from entire networks, and across national
borders. In addition, investments need to be made in terms of data quality, metadata,
communications, networking, and shared access.

Moreover, the currentl
y available data may not be utilized to their fullest extent. For example,
data may be available in real time, but algorithms (or data assimilation schemes) have yet to be
developed in order to make better use of them.
[provide more “meat to the bones” an
d add

A lot of data are being used to initialize numerical weather prediction (NWP) models. The NWP
forecast performance skills depend strongly on how well the observations may resolve
horizontal and vertical gradients in moisture, temperature,
and pressure fields. Moreover, the
model physics representing the atmospheric boundary layer, cloud and precipitation, and

radiation processes require further improvements. For example, higher resolution modeling
demands a better understanding of the phy
sical processes that may have been parameterized
in coarser resolution models. High resolution modeling, such as provided by the High
Resolution Rapid Refresh (HRRR) model, resolves convective
scale processes yielding much
more realistic looking storms th
at may also move at more realistic speed. Such model
advancements, however, have to go hand
hand with development of observational
capabilities to provide relevant data to test model performance against. NWP model prediction
failures can often be trace
d back to a lack of relevant mesoscale observations (e.g., boundary
layer and tropospheric temperature, humidity, wind, and stability profiles).

[add other issues]

Weather phenomena

Wake vortex:

Among the functional requirements is a set of entries concerning wake vortex observation at
designated airports (determine location

horizontal and vertical displacement

dissipation). Although Doppler lidars have been used for this task in research pro
currently there is no plan to deploy lidars at airports for wake vortex detection, nor have
operational products been developed for meeting these specific requirements. Instead, the EA
Weather Roadmap calls for wake turbulence mitigation systems to

be implemented, which do
not provide direct observations of wake vortices, but utilizes wind forecasts to predict their
average movement. In this instance the EA roadmap is not exactly aligned with the NextGen
requirements, thus leaving a gap.

There are some FAA and NASA sponsored research programs that investigate wake vortex
detection (using ground
based lidar) and forecasting. For the latter application, a vertical
profile of winds, stability and turbulence (EDR) are needed. MDCRS data can
provide this
information, although the current turbulence downlinks may not have adequate vertical
resolution for the vortex problem. In addition, boundary layer wind profilers (with radio
acoustic sounders for temperature) could also provide valuable inf

Microburst and low
level wind shear motion:

The requirement to determine the speed and direction of microbursts (as well as low
level wind
shears) is not currently met, nor are there plans to do so for IOC. It is, however, possible to
such a capability utilizing radar
derived microburst detection and storm motion

Vertical extent of low
level wind shear:

Radar observation of low
level wind shear is conducted using only the minimum elevation angle
(surface) scan. Currently

there is no attempt at determining the vertical extent of the wind
shear. In principle, such a determination is possible by utilizing data from multiple elevation
scans, but it would be limited by the radar antenna beamwidth and the viewing geometry. A
Doppler lidar would have the desired vertical resolution, but it is strongly limited in range by
precipitation and cloud attenuation.

Tornado, waterspout, and funnel cloud:

There are separate observation requirements for tornado, waterspout, and funnel c
loud. A
waterspout is a tornado over a body of water (as opposed to over land). A funnel cloud is a
shaped condensation cloud associated with a violently rotating column of air that is not
in contact with the Earth’s surface. It is the separation

from the surface that distinguishes it
from a tornado or waterspout. There is a radar
based product called the tornado vortex
signature (TVS), but it does not distinguish between these three phenomena. Separating over
water vs. overland events is a simp
le matter, but determining if the spinning column is touching
the ground (or water surface) is not a straightforward task. TVS also does not report intensity,
which is a requirement.

developed dust/sand whirls:

The American Meteorological Society (
AMS) definition of a sand whirl or well
developed dust
whirl is a dust devil. Currently there is no sensor product for dust devil detection. A dust devil
has diameter 3 to 30 m with an average height of about 200 m. In general, this is too small and

for resolution and coverage by the existing network of radars as well as satellites. A
specialized, high
resolution (short wavelength) radar might be used for observation, as well as a
Doppler lidar, but a product would need to be developed that distingu
ishes dust devils from
other phenomena.


Falling shafts of hydrometeors that evaporate before reaching the ground are called virga.
Weather radars can observe the precipitation aloft associated with virga, but due to the
elevated minimum beam heig
hts they may not be able to detect a precipitation
free zone
beneath the precipitation aloft. There is presently no sensor product for virga identification.
Such a product could be developed using weather radar data combined with high
ground obse
rvation data of precipitation.


A squall is a strong wind with a sudden onset, duration of the order of minutes, and a rather
sudden decrease in speed. A squall line is a line of active thunderstorms, including contiguous
precipitation areas due
to the storms. The functional requirements have entries that refer to
squall observations (JPDO 2008), while the preliminary portfolio requirements use the term
squall line (Moy 2008). Thus, there is ambiguity about which phenomenon is the subject of the

official requirements. In either case, there is currently no specific sensor product that
addresses the requirements for location, movement, and time. However, there does not
appear to be any significant technical obstacle to developing such a product.

For example, in
the case of a squall line, the convective weather forecast algorithm in the Corridor Integrated
Weather System (CIWS) internally classifies weather into line storms, different types of cells,
and stratiform precipitation.

Gravity Waves:

Gravity waves (or buoyancy waves) are also an aviation hazard not specifically targeted by the
functional requirements. Low
altitude wind shears due to these waves as well as clear

turbulence generated by breaking waves at high altitude are dangers to

aircraft. The National
Research Council (NRC) report on mesoscale meteorological sensing needs (NRC 2008) points
out gravity waves as an important phenomenon for observation. While specific radar products
are generated for low
level wind shear due to mi
crobursts and gust fronts, no such product
exists for gravity
wave induced wind shear; thus, aircraft may be exposed to this dangerous
phenomenon even where there is coverage by appropriate radars (Bieringer et al. 2004).


[not assessed in F

Environmental impacts from aviation
related activities are getting increasing attention.
NextGen will have to be concerned and deal with issues related to noise and emission pollution
near airports, such as exhaust from airplanes taking off or deicin
g fluids getting into ground
water systems. Moreover, contrails provide a non
negligible effect on the radiation balance
and thus climate. Monitoring and prediction capabilities will have to be developed to quantify
and minimize environmental impacts.
may need to provide more details . . .]

Bird strikes and wildlife incursions

Bird strikes have long been recognized as a critical hazard for aircraft. The January 15, 2009
multiple bird
strike event that brought down US Airways Flight 1549 into the Hudson

garnered widespread public attention and angst. Given the high incidence rate of bird strikes,
some view it as only a matter of time before a disaster strikes. Meanwhile, commercial bird
detection radars are available, and research has shown that
data from existing FAA radars can
be effectively used for bird detection (Troxel 2002). And yet, there is no bird detection
[Is there a NextGen bird detection requirement in some other portfolio?]

Although birds are not exactly an atmospher
ic phenomenon, the same sensors and techniques
used to observe weather can be applied for bird detection, so it would be pragmatic to place
this aviation hazard under the aegis of weather observation.

Bird strikes and other wildlife incursions (e.g., tur
tles on runways, as observed at JFK airport this
year on
need date here
) do not show up in the functional requirements (a gap in itself?) and
thus were not particularly addressed as part of the FY09 IOC assessment.

Note that the FAA and USDA have begun a

sponsored program to evaluate the feasibility of
commercial radar systems to detect and track birds in the airport environment. The FY09/10
efforts are focused on the evaluation of one vendor’s system.

Volcanic ash

[not assessed in FY09]

Volcanic eru
ptions may generate ash clouds that reach the tropopause within 5 minutes.
Moreover, these ash plumes may persist for a long time and travel around the globe. NRL and
AFWA have groups that develop capabilities to predict travel of ash plumes. A comprehe
gap assessment for the ash cloud problem requires broader agency participation (i.e., USGS and

[add more information here?]

Space Weather

Space weather impact on aviation is a relatively new field and therefore is likely to contain
nt gaps in both knowledge and sensors. The specified requirements focused on
geomagnetic activity and radiation levels, but neglected the space weather impacts on Global
Navigation Satellite Systems (GNSS) as well as on communications. Current operations

the Space Weather Predication Center (SWPC) only uses one ground base magnetometer as
part of its monitoring of geomagnetic activity. This greatly limits the spatial extent of these

There is a need to evaluate the potential benefit of using GPS occultation (especially GPS/LEO
links) to provide information regarding TEC in the ionosphere.

Weather radar

[this may have to go elsewhere, be reduced or omitted; it seems out of place

Radar data is likely the most valuable resource for forecasters and modelers for weather hazard
detection and prediction. While the NEXRAD coverage, ascertained by 143 mile radii around
the radars, “covers” nearly the entire United States lower 48, 70%

of the boundary layer is
unobserved because of earth curvature and blockage effects. Thus, while being an invaluable
resource, there are also large coverage gaps, which if covered, would provide valuable new

The most commonly known NEXRAD
measurements are the radar moments of reflectivity (Z),
velocity (V) and spectrum width (W). These variables are, however, indirect indicators of
weather hazards. Reflectivity cannot alone distinguish between hail, rain, and ground clutter.
Radar derive
d velocity is a measure of the radar radial speed of hydrometers (not true wind
speed). Spectrum width potentially is a valuable measurement, however, its measurements
has historically been plagued by high measurement uncertainty. Additionally, radar dat
quality is compromised by ground clutter echo, biological scatters, RF interference, second trip
echoes, velocity ambiguities, beam spreading effects and calibration issues.

Vital to the effective use of radar data is 1) data quality control 2) algorit
hm development for
the detection of the weather parameters and phenomena of interest.

There are other derived radar variables of interest besides Z, V and W. For example, NCP/SQI
(Normalized Coherent Power/Signal Quality Index) can be used as data quali
ty indicators.
Recently CPA (Clutter Phase Alignment) has been used for effective ground clutter
identification. Other potential informative variables may be derived from the radar times series
and associated spectra. The point is, radar data is a very
valuable source of weather hazard
information. To use it properly and obtain the information imbedded in the data necessitates
the development of signal processing algorithms for better weather observations and

Dual polarization of the NEXRA
Ds promises to dramatically increase the amount of weather
information available to forecasters and users. However, there will be a need for significant
investment in data quality control, calibration, and signal processing algorithms and verification
unlock this information. High quality, dual polarization data potentially can identify and

differentiate rain, hail, ice crystals, ice, graupal, and biological scatters. Additionally, dual
polarization radar my be able to detect volcanic ash plumes, fore
st fire plumes, and icing
hazard. Any phenomena that lofts or drops about 100 micron sized particles or larger in
sufficient concentrations into the atmosphere can potentially be detected and identified with
dual polarization data. The keys will be 1) in
vestment in signal processing, 2) data quality
control and metrics, 3) availability of other data sources and 4) verification studies. For
example, the quality of the NEXRAD dual polarization data could be established via a field
experiment. If KFTG were

dual polarized its data could be compared to the nearby CSU
and S
Pol, two high quality, dual polarized S
band research radars.



A number of documents have been developed that compile functional, performance and other
requirements. Unfortun
ately, there is a fair amount of work needed to clean up these
documents (e.g., there are a number of odd or unrealistic requirements). It would be
particularly helpful to understand how these requirements came about

i.e., what needs drive
a functional ca
pability and how have the associated performance requirements been
determined. Moreover, the functional and performance requirements have to be synchronized
with the overarching NextGen Weather Integration Plan (WIP); there are some apparent

list examples from turbulence

The FY09 effort included an IOC sensor assessment based on utilizing functional requirements
only. Once the performance requirements will become available, further in
depth analyses will
have to be carried out to
identify gaps, as defined in section 3.3. This will be the focus of the
team’s FY10 effort. Moreover, it will be important to facilitate close participation of data users
and sensor support agencies in this gap identification effort. This might be achie
ved through a
series of focused workshops and meetings that will shed light on particular aviation problems
that need to be mitigated. Moreover, it may also entail observing system sensitivity
experiments (OSSEs) that will elucidate benefits of increased
sensor density and or ways to
combine sensor data fusion with numerical modeling.

The FY10 in
depth gap analyses will likely reveal needs to develop new sensing capabilities, for
example, to augment or replace the human observations in order to meet Next
requirements of a largely automated system. Moreover, it is to be expected that sensor
enhancements will be needed to meet performance requirements, or deployment of additional
sensors required to satisfy spatial coverage. Given the time it takes to
get things into
operations, new key sensor deployments need to start soon. More advanced capabilities
utilizing dynamic adaptation and control may need to be developed to mitigate the occasional
dynamic gap, for example, evolving from a power, communicati
on, or sensor failure. And last,
but not least, there is a need to determine the cost/benefit ratio for “low
hanging fruit” cases in
order to meet IOC/MOC requirements.

[probably many more …]

Space weather monitoring and prediction for aviation applicat
ions will benefit greatly from the
deployment of dedicated sensors instead of relying on scientific NASA missions (
any others?



There will be a variety of opportunities to enhance the current observing capabilities toward
satisfying Next
Gen requirements. Given a limited amount of time and money to get such
enhancements into operations, it will be important to focus on low
cost high
benefits activities.
A number of “low
hanging fruit” opportunities are listed below, albeit without any co
assessment or attempt to prioritize them.

Runway crosswind and windshear

Access to one
second LLWAS data (as opposed to the usual 10 second reports) could
be very useful in determining runway hazards due to turbulent winds. These data
exist o
n the sensors, but are not transmitted.

LLWAS data can be used to estimate runway crosswinds. The sensors and data exist,
all that is needed is algorithms to compute the crosswind, determine if the value is
above what is deemed a hazardous level, and th
en generate a text message that can
be displayed on the current LLWAS ribbon displays.


Increase the number of aircraft that are reporting turbulence (EDR) over CONUS,
and begin deploying the EDR algorithm on aircraft types that fly oceanic routes.
ICAO has already determined that EDR is the turbulence parameter that should be
linked fr
om commercial aircraft. Nevertheless, there is no consistency
between what ICAO recommends (and what the FAA is deploying) and aircraft in the
WMO AMDAR and ASDAR programs.

TDWR data may be used for convective turbulence detection in the terminal area.

Algorithms have already been developed for WSR
88D radars (i.e., NTDA). These
algorithms can be adapted to work on the TDWR data stream.

Although the NTDA algorithm is implemented on the NEXRAD, the data can’t be
accessed at this time. Coordination be
tween the FAA and NWS to facilitate access to
the NTDA data would greatly enhance the turbulence monitoring and forecast
product generation.

Airborne radars are being shipped that have convective turbulence detection
capabilities; however, any alert info
rmation generated by these systems stays
onboard. These data should be down
linked for integration into the GTGN
turbulence nowcasting product being developed for IOC.

NOAA is planning to deploy a number of ground
based GPS receivers. These data
be utilized to calculate turbulence information. (However, the methodology
developed for use with GPS
aircraft links need to be evaluated for their
effectiveness for GPS to ground
based receiver links.)

Weather in cockpit

Technology exists
to uplink weather information (both convection and turbulence)
into the cockpit, which would be highly beneficial for oceanic routes. It would be
straightforward to resurrect earlier uplink experiments and elevate them to more
permanent status.

LWE and

aircraft or runway deicing

Large airports may not have enough sensors distributed across the airport. The
spatial representation of a single snowfall rate or visibility measurement is likely not

The ASOS have only the freezing rain algo
rithm turned on, but others could be
turned on as well.
[need a bit more explanation]

Fog problem

[need a statement here]

Space Weather

A significant early opportunity for space weather related sensor deployment is the
DSCOVR platform that is currently being considered for operational deployment by
NOAA after transferring the hardware from NASA. Maximal utility for NextGen
needs would be
gained if this platform contains both solar wind instruments as well
as a chronograph.

[add more examples from different areas]


Overall lessons learned

There are several efforts underway to assess sensor networks, including NOAA’s evaluation of
observing capabilities or the National Research Council’s network of networks study (NRC

The National Science Foundation (NSF) has compiled an extensive list of
instrumentation and observing networks as well. And the Office of the Federal Coordina
tor of
Meteorology (OFCM) is facilitating workshops to discuss similar efforts across agencies. The
NRC report highlights that a nationwide coordination is needed, but it isn’t clear who will be
emerging as the champion to lead this coordination and how t
he guiding principles (e.g.,
policies and incentives) have to be worked out in order to make this happen in an effective way.

Through the Rights Sizing effort, the FAA is conducting an assessment of its observational
assets. The FY09 effort included comp
ilation of a spreadsheet/database (i.e., sensor catalogue)
that turned out to be tedious work tempting one to easily lose sight of the most important
aspects (i.e., “loosing sight of the forest while studying every tree in it”). Moreover, the FAA is
zing a lot of observations from sensors that it doesn’t own, which requires coordination

across agencies (especially with NOAA, NASA, among others). This coordination across agencies
needs to be enhanced for the overall effort to be most effective.


sensor catalogue provides for a comprehensive list of assets, but it will not be
straightforward to digest that wealth of information (some of it less relevant) in terms of an in
depth gap analysis. Part of the problem is the amount of information collec
ted, but also its

i.e., there are multiple dimensions to the content that cannot easily be extracted
from the spreadsheet/database. Moreover, the performance requirements remain to be
sorted out in the context of the spreadsheet/database

a no
trivial effort in its own right.

A series of focused workshops, including a broad team composition with representatives from
both the weather and aviation user community, will likely be an effective and complementary
way to gather information about pot
ential gaps for specific problem situations. Moreover,
these information
gathering sessions will provide a number of suggestions on how to improve
or mitigate the shortcomings (i.e., “low
hanging fruits” to be explored).

[there may be a few other things

that we can point out . . .]