# Presentation: 450291

## UTILIZATION OF UAS’s FOR GLOBAL CLIMATE CHANGE RESEARCH

- A Summary and Synthesis of Workshop 3

- TABLE OF CONTENTS
- Overview		Page 2
- Aerosols, Chemistry, Trace Gases, and Clouds 		Page 3 - 7
- Carbon Cycle, Water Vapor, Greenhouse Gases and Radiation Fluxes		Page 8 - 10
- Cryosphere		Page 11 - 13
- Observing Systems Simulation Experiments (OSSEs) and       International Observing Systems		Page 14
- Hurricanes		Page 15 - 18
- Appendix:  Key Themes & Challenges		Page 19 - 26
- Contact Information		Page 27

## Overview

- On February 28th and March 1st, 2006, the NOAA Global Systems Division, NASA Science and Aeronautics Research Mission Directorates, and DOE Office of Science sponsored the third in a series of workshops on the Utilization of Unmanned Aerial Systems for Global Climate Change Research.  Participants from NASA, NOAA, the Department of Energy, and other Federal agencies gathered together with researchers, scientists, and engineers to build upon the work completed in the first two workshops. The first workshop developed 11 science goals for UAS, the second workshop then focused on the technology gaps and roadmaps, and this workshop focused on the specific needs of the observation and modeling community.
- This session began with presentations on the use of UAS technology in meteorology and for climate monitoring in remote locations.
- Focused collaborative work then followed, beginning in mixed discipline groups in which participants defined the various terms and processes used throughout the modeling and observation community.
- For the next activity, participants developed a list of data types that need to be collected in the following focus areas:
- •   Aerosols, Chemistry, Trace Gases, and Clouds
- •   Carbon Cycle, Water Vapor, Greenhouse Gases and Radiation Fluxes
- •   Cryosphere
- •   Observing Systems Simulation Experiments (OSSEs) and International     Observing Systems
- •   Hurricanes
- Each group then decided upon 5 high priority missions that could be developed in further detail, for which they defined additional mission parameters.

- For the remainder of the workshop the participants developed instrumentation and vehicle/platform requirements for each mission, and identified possible the gaps in existing technology and an approximate cost for development that would fill these gaps.
- Following the workshop, a small team then developed scenarios for short-term collaboration between NOAA, NASA and DOEto address improving hurricane forecasts.
- On behalf of the organizing team, our sincere thanks to all of the participants for their dedicated and focused work throughout the workshop!

## Aerosols, Chemistry, Trace Gases, and Clouds

| What | Why | Where | Models | Current Periodicity or Frequency | Desired Periodicity or Frequency | How Suitable UAVs |
| --- | --- | --- | --- | --- | --- | --- |
| Clouds
Cloud radiative forcing | Climate forcing form (uncertain) | Clouds and pollution | Climate models
Cloud processing models | Process studies ongoing
Satellite data | Depends on importance of process | Highly suitable
-remote sampling and vertical profile |
| Storm penetration | Hail/Nox/charge production | Strong convection | Climate models
Cloud processing models | Episodic | Depends on importance of process | Highly suitable
-need armor
-customized |
| Hydrological cycle | -ecosystem impact
-human effects
-QPR (quantitative precip forecast) | Global | -weather models
-global | High | High | Highly suitable in part
-adaptive forecast observations
-remote access
-vertical profiling
-multiple UAVs |
| Aerosols
Aerosol budgets
Sulfate/soot/dust/ organic | Climate forcing & air quality & biogeochemical cycles | Global lower atmosphere | Global chemical transport & air quality models | High | High | Highly suitable
-adhoc/event oriented
-cost
-remote access
-enhanced profiling |
| Trace Gases
Antarctic tropospheric nitrogen cycle
Polar bromine production | -surface budgets
-snow firm processes | Antarctica & Arctic (near surface & boundary layer) | Global chemical transport & air quality models | Low | More | Don’t know (worth considering) (range & vertical profiling) |
| Reactive gases: HOx, NOx CIOx, Ox | -Ozone production
-GHG lifetimes
-Air quality | Global troposphere & stratosphere | Global chemical transport & air quality models | Low | More | Highly suitable |
| Chemistry
Chemical influence in aerosols

Nitrogen/Sulfur/ Carbon cycles | Organic emissions can chg aerosol proportions (natural & anthro)
-source strengths uncertain
-biogeochemical coupling uncertain | Global atmosphere & ecosystems | Global chemistry & transport  air quality models | High | High | Highly suitable
-remote access
-great vertical profiling |

**Aerosols, Chemistry, Trace Gases, and Clouds**

**Mission Summary**

## Aerosols, Chemistry, Trace Gases, and Clouds

|  | Mission #1: Cloud RF Properties | Mission #2: Antarctic Photochemistry | Mission #3: African Dust Event | Mission #4: T-Storm Penetration |
| --- | --- | --- | --- | --- |
| Location | Troposphere/Tropics | Antarctica continent | Tropical / subtropical Atlantic Ocean | Continental US / Great Plains or African continent |
| Altitude | Up to 16-18 km | Near surface & boundary layer & free troposphere | Free troposphere (≤ 15km) | Mid-troposphere (≤ 55,000ft)
Upper |
| Duration | Diurnal cycle | Diurnal cycle | Week (3-6 days) -> multiple platforms | 6 hours |
| Payload

Season

Common needs | Size
#
Shape
Chem Comp

Radiative flux divergence
High spatial vasduction
Summer convective season | Reactive nitrogen (NO, NO2, HNO3, PAN, HONO)

Ozone

Photolysis flux

Summer | Size
#
Chem Comp
Chemical tracers: O3, CO, DMS
Radiative flux
Lidar | Chemical tracers: NOx, OC, O3
Hail
Cloud particle probes
Field mils
Particle charge
Winds u,v,w & shear

Summer convection |
| Autonomous

Real time adaptation | High-bandwidth download (real-time, sensor dependent)

Autonomous
Realtime adaptation & programming

Cloud detection | Low-bandwidth (burst)

Autonomous

Preprogrammed Survey | Peak activity in late summer
Satellite comm download
Autonomous
Realtime adaptation & programming | Specialized communication

Autonomous

Realtime adaptation & programming |

- Ice particles & background aerosols

- background aerosols & clouds

**Mission Details**

**Aerosols, Chemistry, Trace Gases, and Clouds**

## Aerosols, Chemistry, Trace Gases, and Clouds

| Missions | Instruments | Instrument Characteristics |  |  |  |  |  |  | Doability |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | Type | Wt | Vol | Viewing ports | Power | Comm | Remote users | Current Manned | Current UAS | Needs UAS (5-10 years) |
| Cloud RF Properties | -size, #, chemical comp of ice & background aerosols
-rad flux div w/high spatial resolution | I/R | L/M | L/M | Y
Y | L/M | H 
Real | Y | 70-90 | 60-100 Global Hawk
Altair | Multi-platform
-inc endurance
-cooperative flying with smaller UAVs |
| Antarctic photochemistry | -reactive nitrogen
NO, NO2, HNO3, HONO, PAN
-Ozone
-photolysis flux | I | H | H | Y
Y | H | L
Burst | N | 50 | 50 | Multi-platform
-icing tolerance |
| African dust event | -size, #, chemical comp, backgrnd aerosols & clouds
-chemical tracers CO, O3,DMS
-radiative flux
-aerosol lidar | I/R | M/H | M/H | Y
Y | M/H | H
Satellite | Y | 80 | 10-100 Global Hawk
Altair | Multi-platform
-inc payloads
-inc endurance |
| T-Storm | -chemical tracers CO,O3,NOx
-hail
-cloud particle physics
-field mils
-particle shape
-winds u, v, w, & shear | I | M/H | M/H | Y
N | M/H | H
Specialized | Y | 0-20 | 0-20 | Armored UAV |
|  | *Chemical composition tend to be large inst/high power
*Increase accuracy - space/wt/power
**Constant instrument density
-density decreasing
•Power scales w/wt and volume | Insitu / remote | 50 low / 300 med / >300lbs high
 | Low/Med/High | Yes/No | Low/Med/High
 | Low/Med/High
 Bandwidth | Network Distribution Realtime | % capability of doing national missions in an optimal way |  |  |

**Sensors and Platforms**

**Aerosols, Chemistry, Trace Gases, and Clouds**

## Aerosols, Chemistry, Trace Gases, and Clouds

| Technical Challenges and Investments |  |
| --- | --- |
| International & NAS access & liability : certify system vs. UAV | Agency / FAA Investment |
| Increased payload diversity | Invest / develop new techniques |
| Option A: larger payload (space, weight, power) | Invest in UAV platform |
| Option B: smaller payload (instruments without loss of performance) | Invest in instrument technology |
| Communication spectrum / bandwidth
(need increased capacity) | Invest / develop new communication systems: spread spectrum |
| Coordination of multiple UAS platforms for mission objectives | Invest in application activity & software control systems |

**Aerosols, Chemistry, Trace Gases, and Clouds**

## Aerosols, Chemistry, Trace Gases, and Clouds

| Priorities | Next Steps |
| --- | --- |
| Systematic approach to:
Developing science / mission priorities
Improving payload / platform options ($) | Synthesize workshop results in white paper |
| Coordination / Cooperation / Collaboration of civil and military UAS priorities / development
Establish commercial use market to lower UAS costs | Identify schedule of events that lead to a desired outcome: e.g. science goal |
| Establish national test-bed fleet of UASs for developing instruments and testing mission concepts | Increase exposure of UAV science technology to the public, program managers, etc. |

**Aerosols, Chemistry, Trace Gases, and Clouds**

## Carbon Cycle, Water Vapor, Greenhouse Gases & Radiation Fluxes

| Phenomenon/ Data Type | Why are these important? | Where found or gathered? | What models used in? | Current periodicity or frequency? | Desired periodicity or frequency? | Usefulness of UAS? | Other |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CO2 (x, y ,z, t, emissions trends | Climate forcing
Stratospheric ozone depletion (CFC’s and N2O)
Source and sink quantification
Air quality (CO & O3)
Health (UV & vis)
Emissions & public policy decisions (short term and long term)
Transport
Is stratospheric water increasing?
The role of convection and upper tropospheric water
 | Vertical profiles especially in boundary layers (1-2 km) and up to 50-60km
Urban
Continental
Oceanic
Global, pole to pole
 | GCMs (general circulation model)
Air quality
Regional climate models
Cloud resolving models
Stratospheric chemistry models
Bio-geochemical models/terrestrial and oceans
Transport models | Seasonal
Very good (hourly) at established background sites (CO2, CH4, CFC3, N2O) | Diurnal (seasonal) for air composition
Improved hourly ozone and water vapor in both time and location
Monthly 24-hr flights
Radiative at least weekly throughout the year
Time with satellite overpass
Focused campaigns to target radiation measurements above and below clouds | Long endurance to look at atmospheric chemistry over time
Long range/cover large distances
Long range: remote locations
Fly over hazardous areas: polar regions, high altitude/pilot safety, plumes
Coordinated flights/multiple platforms (coordinated instrumentation) | In the long term, costs might be less
1-5 yr: a few campaigns focused on clouds
5-10 years many campaigns, many UAVs
Looking at trends: water vapor, O3 |
| CH4, CO, H2O, N2O, CFCs, O3, SF6
State parameters, p, t, wind speed, wind direction, high resolution (for eddy), relative humidity |  |  |  |  |  |  |  |
| Short wave (albedo) |  |  |  |  |  |  |  |
| Longwave,
Broadband,
Spectral radiance |  |  |  |  |  |  |  |
| Microwave radiometry
Landcover classes
Ocean color
Aerosols and clouds |  |  |  |  |  |  |  |

**Mission Summary**

## Carbon Cycle, Water Vapor, Greenhouse Gases & Radiation Fluxes

| Data Type | Location | Altitude | Duration & Range | Season | Data Communication | Autonomy | Real-time Adaptation |
| --- | --- | --- | --- | --- | --- | --- | --- |
| CO2 | South America (Amazon)
North America (carbon sink)
North Eurasia (tundra)
Air/sea exchange open ocean | 1 km to 12-14 km

1 km to 12-14 km

1 km to 12-14 km

<100M | to cover the continent
continental forests

continental forests

diurnal | All

All

All

All | OTH—real time data
OTH—real time data
OTH—real time data
LOS; real-time | Not way-points

Not way-points

Not way-points

Yes, terrain following | Yes

Yes

Yes

-- |
| non-CO2 greenhouse gases | Continental with populations
Polar missions (stratospheric ozone) | 1-21 km

5-21 km | large scale regional
Get over the pole/remote | All

All | OTH or LOS, real-time
OTH, real-time | Not way points

Not way points | Yes

Yes |
| Short wave spectral radiation & long wave spectral radiation | Over the oceans, over & under cirrus, no other clouds (tropical and mid-latitudes)
With cumulus stratus underneath
Satellite overpass coordination | 0.5-18 km

0.5-18 km

Coordinates with the other two | 18-24 hrs

18-24 hrs

Coordinates with other two | All

All

Coordinates with the other two | Real time

Real time

Coordinates with other two | Autonomous option (master/slave)

Autonomous option (master/slave)
Autonomous option (master/slave) | Yes

Yes

Yes |
| Microwave radiometry (in conj with GHGs) (H2O, total H2O, t, soil moisture) atmosphere, rivers | Continental population areas
Oceans | 15 km (no vertical) | to cover continent | All | OTH, real time data | Autonomous option | With real-time adjustments |

**Mission Detail**

**Carbon Cycle, Water Vapor, Greenhouse Gases & Radiation Fluxes**

## Carbon Cycle, Water Vapor, Greenhouse Gases & Radiation Fluxes

| Mission | Instrument Characteristics | Doability (UAS?) | Challenges and Investments | Synthesis/ Next Steps |
| --- | --- | --- | --- | --- |
| 1abc
CO2 (0.1ppm)
Continental 
+
1d
Air-sea (0.1ppm) | NDIR (non-dispersive infrared (100lbs, 1.5x 2.5x 1.5’)
State parameters (30lbs, 1cu ft.) (x, cl, a, t, p, T, wind) same as above
Instant wind speed (30lbs, 1 cu ft)(eddy-correlation method)(1 to 10 sec)Sea surface temperature mixed layer depth | Yes, MALE (medium altitude, long endurance)
Yes

Yes, LALE (low altitude long endurance) | Low altitude, Nat’l airspace~$100k to build + labor
$50k to build + labor

eddy correlation method from LALE is not well knownairframe problems at low alt~$200k (w/labor) | High priority (new CO2 instrument; packaging
Medium

High priority/new CO2/ correlation instrument |
| 2a
Non-CO2
(2% or better)

2b Non-CO2 (polar) | Gas chromatograph (CH4, CO, N2O, CFCs) (GC) 

Tunable diode lasers (H2O, CH4, CO, N2O)100 lbs 1.0x1.5x2.5
UV absorption spectrometer (O3)* 1.5x1.5x2, 100 lbs
Plus mass spectrometer (CCly, CBry) | Yes, done on MALE 

Yes, not done 
Yes 
Yes, not done | 2a: Low altitude, Nat’l airspaceFuture UAV GC mass spectrometer (MS) needed--$300k + labor 
$200K + labor
O3 response needed $200k 
Same as above | High/need new GC-MS 
High/need new instrument
High/need high resolution 
High/new GC-MS |
| 3
Shortwave spectral

Longwave spectral | w/m2/λ & Sλ
Upwelling & downwelling

1% accuracy for SW
0.5% accuracy for LW
<1/4 m3
<30 lbs  <10 lbs (future) | Yes
(Stable) platform (pitch, yaw, roll) New to compensate
IGS?? | Very important: cross comparison to satellite λ resolution and accuracy | Dimensions
Stabilized platform
Design for:
Sustainability, Maintainability
Agility (mission config)
Common Buss (network std)
Time synchronization
Unified instrument GUIs
Automation rules
Local storage |
| 4
Microwave radiometry | C-band/X-band
Brightness temperature
Surface moisture (2-5%)
Cloud liquid water (?)
~100 lbs | Yes: performance need to raise state of measurement science | Accuracy, stability, concurrent radiosondes needed for calibration, interference, noise | Research applications
Verify suitability |

**Sensors and Platforms**

**Carbon Cycle, Water Vapor, Greenhouse Gases & Radiation Fluxes**

## Cryosphere

| Phenomenon/ Data Type | Why are these important? | Where found or gathered? | What models used in? | Current periodicity or frequency? | Desired periodicity or frequency? | Usefulness of UAS? |
| --- | --- | --- | --- | --- | --- | --- |
| 1. Ice extent (2-10)
2. Ice thickness
3. Snow depth
4. Heat and momentum fluxes
5. Moisture fluxes
6. Surface radiative fluxes
7. Vertical distribution of radiative fluxes
8. Vertical distribution of t, p , H2O
9. Surface albedo
10. Composition of snow (soot content)
11. Glacial extent (temporal evolution)
12. Aerosol concentrations, size distributions, composition scattering, single properties
13. Cloud phase, particle shape and SDs
14. PCSs (Polar stratospheric clouds)
15. NDVI (Normalized Difference Vegetation Index)
16. O3, Methane
17. DMS (dimethyl sulfate)
18. CFC, chlorine compounds
19. Salinity of ocean | All: Critical impact on heat balance
Understanding feedbacks in Arctic/Antarctic
Looking at sea level
Understanding feedbacks
Examining Arctic climate

11. Sea level rise, ice sheet extent | 1. Satellite/Air remote sensing
2,3. Surface stations & airborne remote sensors ICESat satellite
4,5. Airborne measurements
6. Surface stations (ARM); airborne measurements to get spatial variability; ship-based sensors, buoys
7. Surface stations and airborne; future satellite?
8. Radio/dropsondes; airborne measurements, satellite
9. Satellite and airborne remote sensing measurements
10. Surface measurements
11. Satellite/airborne measurements
12, 13. Surface & airborne & satellite observations
14. Airborne & ground-based remote sensing
15. Satellites
16. Satellite, ground-based & airborne
17. Ground-based, airborne
18. Airborne
19. Surface buoys, satellite | GCMs
SCMs
CRMs
LES

7. Evaluation of models
12, 13. Inputs to models
4, 5, 8, 9. Inputs to models
1,2,3. Evaluating models | 2. Annually with spotty coverage
4. Rare field program observations
8. Daily at inadequate spatial and vertical resolution
9. Daily, large error bars, with inadequate spatial resolution | 2. complete spatial coverage, sea sonally varying
4. Fine spatial resolution over wide region (100s of kms)
8. Six hour resolution with wider coverage over Arctic to get directive tendencies
9. 10s of ms? | 2. Surveys in extreme environments make suitable
4. Yes, low and slow (surveys)
8. Repetitive, releasing dropsondes makes ideal
9. Ideally suited for scaling studies, “low slow” |

**Cryosphere**

| Cloud and Aerosol Properties |  |
| --- | --- |
| Now | Very infrequent and large error bars from remote sensing retrievals |
| Desired | Fine vertical resolution, in variety of time periods, locations, meteorological regimes |
| UAVs? | Extreme environment
Remote environment |

**Mission Summary**

## Cryosphere

**Detailed Missions**

| Data Type | Location | Altitude | Duration & Range | Season | Data Communication | Autonomy | Real-time Adaptation |
| --- | --- | --- | --- | --- | --- | --- | --- |
| Ice Thickness | Arctic
Antarctic
Greenland
Sample as much of ice area as possible
Temporal resolutions?
Barrow to Norway? | Fly high out of weather
Better spatial coverage | As long as possible to maximize spatial and temporal coverage (routine) | All seasons | Where it is weather that affects operator (landing, take off) | Way points adjustable to weather | No |
| Fluxes | Map out different surface types in the Arctic (different vegetation types, etc.) ice, ocean, snow | Low (10-500 m)
Slow to get small scale and vertical variability | Long to get more spatial statistics (process-oriented studies) | All seasons (very important) | Where it is severe weather has big impact on operations | Identify regions to focus on and adjust due to weather | Yes due to weather |
| t, p, H2O | Locations identified for small scale model studies to better understand processes affecting Arctic feedbacks (synergy with Fluxes and Aerosol). Fine spatial resolutions. | Vertical distribution from surface to stratosphere | As needed to support models & calculations of radiative heating profiles with fluxes | All seasons | Same as Aerosol | Same as Aerosol | Same as Aerosol |
| Surface albedo/ emittance | Same as for fluxes | Below clouds and slow to get fine spatial scales | Same as for fluxes | All seasons | Same as Fluxes | Same as Fluxes | Same as Fluxes |
| Aerosol (cloud) | Need as much data in Arctic in as many conditions as possible (inputs to model and evaluation of model) | Vertical profiles especially through boundary layer but up to tropopause
Vertical profiles of heating to evaluate GCMs regionally and seasonally | Continuous: at a minimum, sample lifetime of cloud systems | All seasons | Very important | No autonomy | Direct aircraft to clouds |

**Mission Detail**

## Cryosphere: Sensors and Platforms

| Mission | Sensor Characteristics | Payload Characteristics | Comm/Network Realtime Rep’t | UAV Char Req’d for P/L | Doable Today? | Challenges & Investments | $ | Surpises |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 1
Ice Thickness | Active/radar (depth) multi frequency (ice snow)
Active/lidar (surface profiling)
Camera (vis, imaging)
Gravimeter magnetometers | 200 lbs (needs development to get this size (~1,000 lb now) | No 
(Yes—avoid bad things)
(data quick look)
 | Large, heavy P/L, long range, low (<5 k ft) | A/C: yes (P-3)
(low altitude global Hawk)
Predator B (poss’bl)
Instr: some development required (miniaturization) | A/C: development for combinations of range and altitude
P/L power possible challenge | $10M

$1M | High vs. Low altitude |
| 2
Fluxes | Radiometers
Hemi. field of view
Up and down welling
Long and short range
Spectral | Now: ~30 lbs

Could be ~10lbs

(100 lb payload tot) | No | Med size & P/L
Long range
Low (<500m) | A/C: yes
Predator A: flight control dev req’d for low altitude
Inst: yes (some dev for miniaturization) | Low altitude autonomous flight | $3-5M

$500K |  |
| 3
t, p, H2O | Dropsondes 
Lidar (T, H2O) 
In situ (t, p, H2O)
Air data computer (winds, TAS, GS) | 1/16 ft3/sonde x/hr  
Diff absorption—currently large
Small, <1 lb
1/8 ft3 ~2 lbs | No | Large, high altitude through troposphere (large altitude range) | A/C: Yes (Global Hawk)
Inst: Yes (dropsonde & dispenser need development—size, cost) | Getting and affording Global Hawk | ~

$500K | State of development of miniaturized dropsonde & dispenser |
| 4
Surface albedo/ emittance | Radiometers (see 2, above)
IR therm |  | No | Med size, low altitude | See 2, above | See 2, above | See 2 |  |
| 5
Aerosol (cloud) | Cloud μphysics | CAPS, CPI, CIN, (in situ) ~70 lbs similar to “glory” instruments (remote sensing)
Aerosol polarimeter
Cloud Radar
Cloud Lidar | Comm & report: yes (cloud finding)
Intelligent mission mgmt (autonomous)
Would probably need supplementary sensors (e.g., ground-based or satellite cloud radar) | Med size, long range, altitude range (for In situ cloud measurements) | A/C: Yes (?) Predator B? (could be stacked A/C) anti-deice equipment
Inst: Yes. Need some inst. development for jav applications | A/C mods for profiling, range & arctic environment
Intelligent mission mgmt/sensor network
A/C power reqts possible issue | $10M

$2M |  |

**Cryosphere: Sensors and Platforms**

## Observing Systems Simulation Experiments (OSSEs) and International Observing Systems

| Issues | Use for UAV Programs | Benefit | Thorpex | Future |
| --- | --- | --- | --- | --- |
| Nature Run (realism, model vs assimilation type of work system)

OSE

WSR | Demonstrate impact on forecast
Test scenarios (mix of observing systems; regulatory environment)
Cost effectiveness against other systems
Command and control examples | Inexpensive
Test scenarios
Prepare op system for UAV data (especially data assimilation; also defining model configuration - i.e. nesting, etc.)
Define areas of greatest sensitivity | Role of UAVs in Thorpex
Smart deployment of observing systems
Sensitive areas are often in remote oceanic/arctic and cloudy regions - UAV territory
Long range/endurance
Loiter
Altitude (low & high)
Multi-instrument capacity
Rapid update on payloads | Very long endurance (months)
Swarming & specialized systems
Mother-daughter ships
Drift & tethered balloons
Expendibility option |

**Mission Summary**

## Hurricanes

| Research | Operations |  |  |
| --- | --- | --- | --- |
|  | Operational Model Input
Operational Model Verification | Very Low Altitude
(for forecasters) | Manned Systems
(Recco) |
| Resolutions
Time frequency
Electric fields
Wind—4D@storm scale over life cycle; resolution; various environments
SFC flux in core (high resolution)
Microphysics
Wave information
Sea spray
Thermodynamic structure (temp & moisture)
Precipitation
Salinity
Loop currents
Upper ocean profile | Hurricane Scale
Winds
Thermodynamic structure
Upper ocean profile

Environmental Scale
Wave fields/spectrum
Conventional observation | ≤ 300m (lower is better)
Conventional measures (wind, p, t, rh)
~6h period (minimum)
Real-time comms (~30 min)
Special flight patterns
Loiter in eye
Eyewall circuits
High density by quadrant
Continuous along flight track
Life cycle
Highest priority within 45 h of coast
Wave info
Sfc. flux in core
As verification of analysis, model forecasts, official forecasts | Supplement or replacement
1, 5, 10, 30+ kft flight level
Wind, p, t, rh, z, surface SFMR
Expendables (dropsondes, AXBT, AXCD)
Current (W of 55 ̊W)
New (other locations)
Continuous along flight track
Sub surface
Ocean surface
For models
 |

**50 km**

**2 km**

**Mission Summary**

## Hurricanes

| Operational
Very Low Altitude (for Forecasters) | Very High >60 kft | Very Low (≤300m) |
| --- | --- | --- |
| ≤ 300m (lower is better)
Conventional measures (wind, p, t, rh)
~6h period (minimum)
Real-time comms (~30 min)
Special flight patterns
Loiter in eye
Eyewall circuits
High density by quadrant
Continuous along flight track
Life cycle
Highest priority within 45 h of coast
Wave info
Sfc. flux in core
As verification of analysis, model forecasts, official forecasts
 | Research
Winds/precipitation
Doppler radar, lidar
μmicrowave radiometer: usual + high freq >50 gHz; precip/moisture
Lidar—temp, moisture—high vertical resolution, virtual satellite
Electric field
Scatterometer: Sfc. wind
Satellite calibration/validation
Sensor development

Operational
Long term climate change
Vertical resolution
Satellite gap filler
Mother-daughter ship
Communication with low level UAV
Real time communication to operational community relay | Research
Thermo structure: inflow layer changes follow inflow
Sea spray
Sfc. flux in core
Upper ocean profile

Operational
Model    asset limitation     non-model

1km or less—horizontal
100m or less—vertical
25-500 km (domain range)
Continuous lifecycle
2-4 times a day |

**1 + 1’eyewall circuits**

**1’ model init**

**3 eye loiter**

**4**

**50 km**

**Mission Detail**

## Hurricanes

**Sensors and Platforms**

| Very High | Weight |
| --- | --- |
| 1. Doppler radar / precip / wind/ structure / surface scatlerometry | 200 lbs. |
| 3. SFMR - sea surface wind | 60 lbs. |
| 4. Moisture measurements
Dial lidar
Microwave radiometer | 400 lbs.
250 lbs. |
| 5. Rainrate
Microwave radiometer
Radar | 250 lbs.
200 lbs. |
| 6. Dropsonde - launch system; wind thermodynamic profiles; upper ocean profile; particle measuring | 1 lb / sonde
120 lbs. / launching systems |
| 7. Communication links & relays | 30 lbs. |
| 9. Cameras - storm damage assessment | 60 lbs. |
| 10. Visible / IR | 15 – 300 lbs. |

*** Longer term technology investment**
*** Survivability requirement**
*** Data recoverability**

## Hurricanes

| Medium Height: 20-45k feet |
| --- |
| Camera - National Geod. Survey
Hindcast storm surge
Storm damage assessment |

*** Survivability requirement**
*** Data recoverability**

**Research **
**Environmental sensing**

| Very Low: less than 300m (in hurricane) |
| --- |
| HP #2 - Insitu sensor - state variables & wind (2lbs.) Wave
*Laser profilometer (1lb.)
*Scanning radar altimeter (100 lbs.)
SST - downward IR sensor (.5 lbs.) |
| Sea Spray instrument (.5lb)
HP#3 - insitu flux - BAT probe (41 lbs.)
Maximum payload - 44 lbs. / AXBT
Minimum payload - 8 lbs. no AXBT, drops, no altimeter
*3D radar
Communication partnerships NASA/NOAA |

*** Survivability requirement**
*** Data recoverability**

- non-model

- model

- research

| Very Low |
| --- |
| 1km or less - horizontal |
| Continuous lifecycle |
| 2-4 times a day |

**Sensors and Platforms**

## Appendix: Key Themes & Challenges 

- During the first round of work, groups explored the key themes of the workshop, and examined issues such as terminology, objectives, questions, and modeling methods and issues across the entire range of workshop topics.
- The tables on the following slides present a combined summary of their thoughts concerning the current challenges facing those who are involved in modeling for:
- •   Aerosols, Chemistry, Trace Gases, and Clouds
- •   Carbon Cycle, Water Vapor, Greenhouse Gases and     Radiation Fluxes
- •   Cryosphere
- •   Observing Systems Simulation Experiments (OSSEs)
- •   Hurricanes.

## Modeling and Observation Matrix: Aerosols, Chemistry, Trace Gases & Clouds

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Aerosols, Chemistry, Trace Gases, & Clouds

(1 of 2) | Understand the spatial & temporal signature of aerosols, chemistry, and trace gases and the implications (short and long term)
Understand trends
Understand coupling
Understand role & importance to climate
Understand sources and sinks of trace gases and aerosols
Understand transport, especially vertical
Understand redistribution of water vapor
Urban pollution
Origin & distribution / phase detection | Aerosol direct effect
Aerosol indirect effect
Cloud lifecycle, albedo, particle size
Particle size
Reducing uncertainty of direct, indirect, and semi-direct effects
Urban pollution
Origin & distribution/xxx detection
Transformation
Feedback
Connections
Indirect vs. direct effect on climate
Radiative properties of aerosols
Biological cycles
Effect of aerosols and trace gases on clouds and precipitation (hydrological cycles)
Determining distribution of a relevant spatial and temporal scales.
UAVs potential to loiter & provide isolate & routine observations; targeted & hazardous. | “Relevant Time”
3-dimensional
Horizontal & vertical resolution
SO2 & CO2
Single-scattering properties
Reactive gases x (Nox, OH, etc)
Temperature & H2O in troposphere
Composition (sulfide, carbonaceous)
Size distribution & dependence on RH, altitude, location & meteorology
PSCs, lifetime & persistence
Determining distribution of relevant spatial and temporal scales
UAS potential to loiter and provide remote and routine observations; targeted & hazardous
Chemical composition of aerosols
 | Spatial resolution 
Temporal Resolution
What is the state of the art & future capabilities?
What data are most useful to validate models?
Validate existing models on regional basis and model inter compason.
Magnitude & sign of climate forcing
Role of pollution to climate & vice versa
Radiative role of clouds
Subgrid scale effects(vertical velocity effects)
Chemistry of air pollution
Aerosol impacts on diurnal cloud cycle
Aerosol impacts on hydrologic cycle
Better representation of ice nucleation
Physical and dynamic processes
 |

## Modeling and Observation Matrix: Aerosols, Chemistry, Trace Gases & Clouds

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Aerosols, Chemistry, Trace Gases, & Clouds, continued

(2 of 2) |  |  | Adequate spatial & temporal availability
Composition and size 
Profiles
In situ and remote
Radiative measurements
Physical & dynamic processes
Chemistry
Parameterized vs. explicit

 | Forecast climate and global warming
Climate - indirect effect
Hydrological models
Regional and global models / scale interactions 
Regional and global models / scale interactions
Convection modeling improvements
Nucleation processes
Scale-dependence IT capabilities |

## Modeling and Observation Matrix: Carbon Cycle

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Carbon Cycle, H2O(g), Greenhouse Gas

 | Understand storage & transport of carbon species between land, ocean, & atmosphere
Understand trends
Budget of C & ghg (sources & sinks) upp air processes for H2O)
Role & importance to climate
Understand carbon sources & sinks 
Couple surface characteristics of water cycle to boundary layer properties
Understand the vertical dynamics
Forecasting the impact:  xxx, intensity, & structure
Relationship between climate change xxx
Flux & exchange:  biosphere/ atmosphere/ ocean

 | The ability to scale up flux tower measurements
Better land cover-land use physiological functional groups
Better understanding of ocean sequestration
Cloud processes that control H2O (g)
C:  Sources & sinks
Where do emissions come from? - Point source identification
How to control emissions (eg., carbon tax)
Remote sensing
Lidars
NWave
Radiometry
Forecasting the impact:  track, intensity & structure
Relationship between climate & hurricanes
Flux & exchange:  biosphere / atmosphere / ocean

 | In situ CO2, CH4, CO temperature, winds (isotopes)
Ocean color
Vegetation types and phenology (seasonality) and structure.
Global distributions
H2O especially in vertical
C especially on continental scale
H2Ov & GHG concfs
Carbon cycle gas  concfs
Where?  Boundary layer important
Reference molecules (CO & Rm, C2, Cl4) concfs to do emissions
Industrial & regional inventories
Where are they?
Where are they going?
3 dimensional data
UAV potential to take boundary layer measurements
Vertical fluxes near the surface
Loitering capability - tracking, remote sensing, in situ measurements
 | Better coupled atmosphere / ocean / land carbon models
Improved process models
H2O(g)
C:  data sparse
Regional effects of GHGs
Modeling will lead to emission estimates
Change in stratospheric water due to methane increases
Compliance with international agreements (Kyoto, Montreal)?
Spatial resolution required?
Temporal resolution required?
What is state of the art?
Future capabilities?
What data are most useful to validate models?
Weather data assimilation
Model resolution
Ocean interaction
Climate change resolution
Chemical - physical processes + assimilation
Ocean/biosphere/atmos-phere coupling |

## Modeling and Observation Matrix:  Radiation Fluxes

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Radiation Fluxes

 | To understand Earth radiation budget
Upp air trends & processes
Improve vertical resolution of r
Understand radiation above and below cloud types
Role & importance to climate
Understand the short wave and long wave radiative flux balance from TOA to the Earth’s surface
 | Vertical radiation
Understand feedbacks between convection, clouds, H2Ov, Radiation and temperature
Effect on health?
Net radiometry, heating rates, surface albedo, BDRF (bi-directional reflective function)
Radiation balance: cloud-aerosol feedbacks | Vertical
Vertical profile of radiative heating thru clouds & atmosphere from bl to tropopause of high temporal & spatial resolution in key locations.
UV radiation monitoring - effects on health?
Correlated data as functions of altitude
Cloud macro & microphysical properties
Measurements of net radiometry, heating rates, surface albedo, BDRF (bi-directional reflective function)
 | Verify / develop radiation budgets within the atn.
How do cirrus properties depend on intensity of convection?
Understand ice nucleation
Spatial resolution required?
Temporal resolution required?
What is state of the art?
Future capabilities?
What data are most useful to validate models?
Coupling with atmosphere radiation balance: largely climate |

## Modeling and Observation Matrix:  Cryosphere

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Cryosphere

 | Understand mass balance of ice on Earth surface and the causes of expansion and contraction
Snow & ice mass balance 
Understand ice/albedo & cloud/albedo feedbacks
Role & importance to climate
Characterize and understand the evolution of Arctic / Antarctic surface processes
Climate change impact on weather | Feedback between snow cover, temperature, clouds, ocean-ice interactions, biosphere and H2O - aerosols
Ice mass - thickness -content - texture  
Surface albedo | Ice thickness - less than 1/2 km spatial
Source
Glacier retreat
Snowpack & sea ice measurements
Aerosol concnf & composition in atmosphere & on ice surface
Surface fluxes (eg leads)
Cloud macro & microphysical properties | Effects of snow cover on climate?
Cloud-aerosol feedbacks
Ocean/cryosphere interaction /albedo |

## Modeling and Observation Matrix:  OSSE

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| OSSE
(Observing Systems Simulations Experiment)

 | Determine impact of data on forecasts, within the context of current and future observing systems
Improve forecasts
Benchmark information impact on modeling performance / fidelity
Targeted observations
Adapted sampling
1-5 vertical presence
5-10 increased autonomy
 | Added value, eg cost/benefit ration of new data
Reduce uncertainty in IC for forecast - where & what, duration of observations needed to improve 5 - 7 day forecasts
Parameterizations
Initial conditions
Boundary values
Observation sets 
Physics / analysis validation
Tailored “value-added processing”, appropriate times and schedule
Current sensors (miniaturization & cost)
Situational awareness
Relevance of OSSE to real world: scientific & operational
 | All atmosphere/ocean data, all scales
Data over Pacific Ocean
Vertical profile of T, H2O, winds, eg, GUPs
Data sparse areas 
Sensitive regions (cloud & weather) Define measurement strategies and UAV configurations & system design, command & control; legal & regulatory | Error characteristics of observations
Data availability (real time)
Data assimilation techniques
Data impacts & benefits
Testing tool |

## Modeling and Observation Matrix:  Hurricanes

|  | Objectives | Key Issues & Questions | Key Data | Modeling Methods & Issues |
| --- | --- | --- | --- | --- |
| Hurricanes

 | Advanced understanding of hurricane science for advanced forecasting: Improved Forecasts
Operational: improve analysis accuracy:data > modelsdata > forecasts
Improve forecast accuracy
Extend forecast horizon
Introduce non-deterministic approaches
Forecast cyclogenesis
Understand & predict short term weather & long term climate trend
Recon (in storm intensity)
Surveillance (around storm track) | Track intensity, structure, rainfall, waves, storm surge
Representing inner core
Obtaining low level data
Representing BC precipitation
Improve forecasts for intensity, landfall & QPFs (72 hours?)
Climate impact on hurricanes?
Intensity change
Landfall impacts
Tracking accuracies
Quantitative precipitation forecasting
Social science impacts
Lack of knowledge of lower BL and upper ocean
Measure turbulence
Accurate true surface winds
Sea spray effect
Sea surface temp (core; mixed layer)
Persistent data gathering
Cost/safety/value | Vertical profiles large and small scale
Temperature, winds, moisture, precipitation, sea surface temperature & structure, wave spectrum, oceanic currents (in situ), surface wind distribution, salinity
Upper ocean
Low level winds
Energy flux
H2O flux
Vertical profile of winds, RH, T, P, SST, 
Clouds microphysical vertical profiles of latent heating
Radiation measurements above clouds
SST
Water vapor distribution
Wind speed & vertical profile
Convection patterns
Synaptic environment conditions | Better parameterization of physical processes suitable for coupled air/sea prediction systems
Initialization of hurricane models
Coupling of hurricane models with land/ocean models
Data assimilation
Model resolution
IT constraints
Model physical parameterization + direct representation
Validation of HWRF
Improved parameterization of HWRF?
Data assimilation techniques- Spatial- Temporal- No homogeneity set
Data fusion
Data availability
Limitations of today’s models
Potential new data sets |

## Contact Information

- For further information please go to: _[http://uas.noaa.gov/workshops/](http://uas.noaa.gov/workshops/)_
- You may also contact:
**NASA **
- Randy Albertson
- (661) 276-7540
- randy_albertson@dfrc.nasa.gov
**NOAA **
- Sara Summers
- (303) 497-4221
- Sara.Summers@noaa.gov
**DOE **
- Will Bolton
- (925) 294-2203
- wrbolto@sandia.gov

**Facilitation and Documentation by InnovationLabs**
- _[www.innovationlabs.com](http://www.innovationlabs.com/)_
**Contact: Langdon Morris - 925 934 1786**