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Mr. Robert E. Dumais, Jr. U.S. Army Research Laboratory CISD BE Boundary Layer Meteorology Branch 2003 BACIMO: 09 Sep 20 PowerPoint Presentation
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DEVELOPMENT OF A HIGH RESOLUTION METEOROLOGICAL NOWCASTING SYSTEM FOR THE U.S. ARMY OBJECTIVE FORCE. Mr. Robert E. Dumais, Jr. U.S. Army Research Laboratory CISD BE Boundary Layer Meteorology Branch 2003 BACIMO: 09 Sep 2003 U.S. Naval Research Laboratory Monterey, CA.

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slide1

DEVELOPMENT OF A HIGH RESOLUTION METEOROLOGICAL NOWCASTING SYSTEM FOR THE U.S. ARMY OBJECTIVE FORCE

Mr. Robert E. Dumais, Jr.

U.S. Army Research Laboratory

CISD BE

Boundary Layer Meteorology Branch

2003 BACIMO: 09 Sep 2003

U.S. Naval Research Laboratory

Monterey, CA.

slide2

DEFINITION OF NOWCAST

  • Often appears to take on different meanings based on individual perceptions
  • A basic description by Browning (1982): “The term nowcasting symbolizes an observation-intensive approach to local forecasting, with timely use of current data, in which remote sensing plays a dominant role”
  • Emphasis more towards the “now” part of the time spectrum
slide3

WHY IS IT IMPORTANT ?

MM5 Forecast Model –

4D Data Cube

20 km

80 km

30 km

current and extended range trajectories

chem/bio

mission execution

acoustic and electro optical propagation and inherent signatures

urban war environment

scenegeneration

Is there an Army “Nowcast” requirement? - Concepts for SWO / IMETS-light in Combat Brigades - IMETS ORD - Relevance to Objective Force/FCS INTEL - New decision aids and weather products for echelons below brigade - Dismounted Battlespace Battle Lab

slide4

WHY IS IT IMPORTANT ?

profilers

Nowcast can be run near battlefield edge to improve UAV operations

uav, ugv, ucv

SHADOW TUAV

lessen bandwidth concerns associated with reachback for timely localized guidance

Dropsondes & balloons

Many new sources of non-conventional battlefield meteorological and surface data to be available (satellite EDRs, dropsonde, profilers, doppler, surface sensors, deployable met towers, etc.)

Surface robotic sensors

Surface met measurements

satellite

slide5

FCS - A “sensor rich” environment

C2, Brigade ISR collection & analysis, UAV/UGV and sensor webs

  • Develop capability for IMETS-light and 2-person
  • SWO Combat Weather Teams in MI CO
  • Support C2 “on-the-move” with up to date met data,
  • met-sat overlays and weather impact decision aids
  • fully compatible with CTP and C4I systems
  • Continue to exploit reach-back for “top-down”
  • weather (from DIV and above IMETS and AF
  • Weather Hubs) for 24 hr and 5 day forecasts
  • Fuse higher echelon forecasts with
    • local sensor obs
    • remote sensing (METSAT)
    • weather HUMINT
  • Improve first-in and daily mission support
    • 0-3 hr local nowcasts
    • 0-48 hr regional forecasts (MM5)
    • 3-5 day theater forecasts (NOGAPS)
  • Support Brigade ops, UAV, RSTA & weather
  • over complex terrain and MOUT
new capabilities are needed to exploit the potential web of sensors on the future battlefield
New capabilities are needed to exploit the potential web of sensors on the future battlefield

Data Fusion of information from Combat Weather Teams, G2, and other soldier observations including weather HUMINT: “A heavy rain has begun now where we are”

APT ~ 4 km/pix,

Dynamic

Dust Storm

HRPT ~ 1.1 km/pix

Dynamic

Soil Moisture

Dynamic Snow Cover

Polar and geostationary WxSAT (GOES, DMSP, NPOESS, …)

UAV derived data (ground vs air speed, snow cover, visibility…) and perhaps direct sensor data

Robotic Ground Vehicles may collect and use local met data - also should exploit NBC RECON met data, armor wind/pressure data, etc.

Surface sensors: IREMBASS-compatible TACMET II sensor and ceilometer; Remote Miniature Weather Sensor (RMWS - Infantry and AF Combat Weather), Miniature Remote Weather Station (MRWS - SOF), Meteorological Automated Sensor and Transceiver (MAST); & AF TACMET-MOD II, Air Traffic Control Towers, Airfield Automatic Met Equipment, AF Marwinsonde profiles when in theater

Arty-met surface and profile observations from AN/TMQ-50 Semi-Automated Meteorological Station (SMS) and AN/TMQ-41 Meteorological Measuring Set

exploitation of next generation metsat
Exploitation of Next generation METSAT
  • Imagery
  • Sea Surface Winds (Speed and Direction)
  • Sea Surface Temperature (SST)
  • Atmospheric Vertical Temperature Profile
  • Atmospheric Vertical Moisture Profile
  • Cloud Base Height
  • Cloud Cover/Layers
  • Pressure (surface/profile)
  • Soil Moisture (Surface)
  • Precipitation Type/Rate
  • Suspended Matter
  • Cloud Optical Thickness
  • Snow Cover/Depth
  • Land Surface Temperature
  • 15. Precipitable Water
  • Cloud Top Temperature
  • Cloud Top Pressure
  • Aerosol Optical Thickness
  • Cloud Ice Water Path
  • Vegetative Index
  • Aerosol Partticle Size
  • Cloud Liquid Water
  • Albedo (Surface)
  • Cloud Effective Particle Size
  • Total Water Content
  • Ocean Wave Characteristics
  • Active Fires
  • Surface Type

Proposed NPOESS Low Rate Data (LRD) - 3.5 to 4 Mbps satellite broadcasts will support future products. Current draft of priorities include these Environmental Data Records:

NPOESS consolidates DoD DMSP and NOAA POE Satellite programs

  • Evaluation of Aerospace Corp Direct Receive Antenna -
  • Prototyping a low resolution solution - NPOESS L-Band Low Rate Data Channel surrogate is DMSP RDS (S-band). Aerospace is developing an antenna/receiver potentially suitable for Army “direct receive” capability deployed at small units
  • Potential as antenna for Mobile Weather Imagery Receipt
  • Supports current weather during comm-sat loss & ARTY-MET
  • “Low cost” horn antenna packaging analog electronics and receiver on the structure with “on-board baseband encrypted interface”
  • Requires tracking mount, PC with mission software and crypto device

Using DMSP F-15 Real-time Data Smooth (RDS) 2.5 km Line Scan and 15-50 km SSM/I and 45-175 km SSM/T as a NPOESS surrogate for EDR data

slide8

34 Vehicle Mounted IMETS

92 Light Systems

Contrasts in Weather Technology for DIV and Above Vs BDE and Below

slide9

Key Components of the Prototype Army Nowcasting System

A NWP model with 4DDA (or possibly 3DVAR) that can be rerun on an 1-3 hourly cycle at mesogamma scale resolution (2.5 km) and for a Brigade-size 150 km x 150 km domain, to produce updated high quality local meteorological background fields (can ingest high temporal and spatial resolution local data sources)

 A fast objective analysis module that can produce 15-20 minute “updated” 3D analyses for sub-Brigade size (20 km x 20 km) local domains nested within the 150 km x 150 km NWP domain, using a blend of the most recent NWP output & the previous objective analysis output for the 1st guess background fields (along with fusing the most recent local observations), and remaining static until the next update analysis

For operational lower echelon application,ACCURACYmust be balanced againstTIMELINESS

slide10

Key Components of the Prototype Army Nowcasting System

  • NWP model : NCAR/PSU MM5 V3 for Brigade-size region
  • - single nest, with background fields for initial and lateral boundary conditions from AFWA operational 15 km MM5 output
  • - 2.5 km, 61x61x31
  • - 3 h forward integration
  • ASSIMILATION OF LOCAL DATA INTO MM5
  • * Use remote sensing to update terrestrial fields in MM5 REGRID module, such as skin/sea surface temperature and binary snow cover
  • * IF MM5 4DDA/Observational Nudging: will require a dynamic assimilation period, and observations arranged in MM5 MMOBS_DOMAINx binary file format
  • * IF MM5 3DVAR: will require appropriate constraint selection for mesogamma scale, and data arranged in MM5 “Little R” input format

- Seeking best compromise between complexity, timeliness and accuracy

slide11

Key Components of the Prototype Army Nowcasting System

Local (20 km x 20 km) objective analysis for 1st generation IMETS nowcast

  • - A 3D objective analysis based on either a successive corrections or a Bratseth scheme, nested within the 150km x 150km NWP domain
  • The analysis updates use the background fields from the 3 h NWP forecast, in addition to local observations (predominately surface sensor, dropsonde, or UAV/UGV) since the last analysis
  • Observations may be “averaged” or “superobed” since last analysis time
  • Analyses are updated every 20 minutes
  • Analysis likely to be at the same mesogamma resolution as the NWP model (2.5 km)
  • Analysis remains static until next analysis cycle
  • - High quality, high resolution background field data from the nowcast mesogamma NWP model is critical to the accuracy of the local analysis!
slide12

TACTICAL ARMY NOWCAST STRATEGY

DTED terrain

BRIGADE DOMAIN

metsat EDRs

150 x 150 km

MM5 mesogamma NWP forecast

(with 4DDA) every hour

Profilers and lidars

AFWA 15 km MM5 fields

local surface observations

Terrestrial via remote sensing

Radiosondes,dropsondes, & pibals

PLATOON DOMAIN

Multiple platoon domains are possible with Brigade domain

20 x 20 km mesogammaobjective analysis

(every 15 min)

Previous objective analysis

UAVs

Inputs going to objective analysis module

NOWCAST CYCLING

Inputs going to NWP forecast module

slide13

Local MM5 runs (Brigade size domain)

An example of a 2.5 km resolution MM5 run over Utah using AFWA 15 km MM5 as initial and lateral boundary conditions (3 h fcst)

Primary purpose: Provide 1st guess background for platoon size, 15 minute update cycle, objective analyses

1st guess and lateral boundary conditions from AFWA operational MM5

Brigade & below local battlefield observations: surface, raobs, dropsondes, UAVs & UGVs, satellite, deployable towers

Rerun hourly ( 3 h forward integration) with observational nudging 4DDA or 3DVAR- single nest configuration

Ingest updated skin/sea surface temperature, snow cover, & perhaps other surface fields from recent satellite passes

slide14

Successive Corrections Objective Analysis Development

- Surface objective analysis is now combined with upper air objective analysis ( using similarity theory)

WSMR SAMS network observations

Will design with platoon-size applications in mind; 10-15 min updates

Possible PDA application

Mesogamma resolution MM5 1st guess field

Mesogamma wind field after objective analysis

slide15

Other Prototype NOWCAST Components

  • A diagnostic microscale boundary layer and/or surface model coupled to the objective analysis fields to provide higher resolution near-surface detail
  • Postprocessing algorithms for the objective analysis output
  • JAVASPACES-based system to ingest & stage regional and local observational and model-derived input meteorological data sources for NOWCAST (including its own output)
slide16

Couple Objective Analysis Fields to a Surface Layer Microscale Diagnostic Wind Model

higher resolution terrain effects on surface winds can be incorporated diagnostically

57.5 km (2.5 km grid)

1.4 km (0.1 km grid)

Ft. Benning, GA in August 2000. Wind arrows and streamlines on both graphs, contour lines for small area (right). The smaller area will fit inside the small square on the left image (3 x 3 km). The microscale output shows large changes in wind speed and direction within the area represented by one wind arrow on the mesoscale output.

slide17

FUTURE: EXTEND SURFACE DIAGNOSTIC WIND MODEL TO 3D

Mass consistent; solve the functional:

Cartesian coordinate and multigrid numerical solution technique (major computational speed up)

Flow partitioning using bulk Richardson number

Includes effects of topography, and small surface features such as forests and buildings

3DSTAT is not an LES or CFD !!

slide19

METSPACES Concept for Battlefield NOWCAST Data Management

- customized implementation of Sun Microsystems’ JavaSpaces networking technology

- coordinating, collecting and disseminating of the various types of data products generated by NOWCAST

- In a networked shared-memory environment, the various types of Nowcasting components can be loosely-coupled with one another through simplified data exchange mechanisms

- primary intent of this environment is to shield sophisticated legacy systems by establishing an abstract interface through which they can automatically interact with prescribed data

slide20

Preliminary Nowcast Proof of Concept Study

Data Set - Three months of Oklahoma Mesonet Observations:

22 Jan- 28 Feb 2000 (38 days); 1 May - 29 Jun 2000 (60 days)

Applies simple data fusion of actual surface sensor observations with the standard 0-24 hourly outputs from BFM, the current tactical Battlescale Forecast Model used in the field on the AN/TMQ-40C Integrated Meteorological System (IMETS)

Reports the absolute forecast error averaged over the other 96 Oklahoma surface stations at each hour of the day (0-24) and then averaged over the 38 and 60 day study windows

Clearly demonstrates the progressive improvements to BFM by fusing 1, 3, 5 and 12 on-scene, real-time surface observations from a few surface observations scattered across the Oklahoma Mesonet to produce a prototype 0-hr nowcast for each hour of the day:

% Reduction in Absolute Error

Using: 1 Observation 3 Observations12 Observations

Temperature: 25 - 30 % 40 - 45 % 60 - 65 %

Wind Speed: 15 - 25 % 25 - 35 % 30 - 45 %

Wind Direction: 10 - 20 % 20 - 30 % 35 - 45 %

Relative Humidity: 0 - 25 % 20 - 35 % 40 - 60 %

Additional improvements are expected when the observations can be assimilated directly into a mesoscale numerical model (as opposed to just the data fusion post-processing used here)

slide21

Future: Improved Response Filter for Objective Analysis

  • Improve handling of irregularly distributed observations
    • Linear least squares problem
      • Find weights that produce desired response function
      • Take derivative with respect to weights
      • Invert matrix for weights

* Slide borrowed from Dr. Mark Askelson, University of North Dakota

response filter
Response Filter
  • Example
    • Coherent input field looks incoherent after analysis (upper right) owing to impact of irregular data distribution the response function.
    • Want to improve technique so that result is coherent (lower right)

* Slide borrowed from Dr. Mark Askelson, University of North Dakota

slide23

POSSIBLE FUTURE EFFORT WITH CG/AR:

Ensemble Data Assimilation

  • ■ Develop and Assess Capability of the Maximum Likelihood Ensemble Filter (Zupanski, 2003) Data Assimilation approach on the meso-micro scales
  • suited for highly nonlinear operators
  • - no adjoints
  • - no assumptions about observation correlations
  • - perturb analysis rather than individual obs
  • - optimize non-quadratic cost function, using a
  • superior Hessian preconditioner
  • - allows for both an estimate of uncertainty and for
  • the most likely deterministic solution