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Introduction. Zoltan’s information Matrix (consider a list of people in the branch, maybe take the time to go around the room for brief introductions) Strategic plan Thrust areas Mission . LAPS System. LAPS Motivation. High Resolution (500m – 3km), rapid update (10-60min)

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Zoltan’s information


(consider a list of people in the branch, maybe take the time to go around the room for brief introductions)

Strategic plan

Thrust areas


Introduction 2336291

LAPS Motivation

  • High Resolution (500m – 3km), rapid update (10-60min)

  • Highly portable system – about 150 users world wide

    • Federal Gov’t – NWS, RSA, PADS, FAA, DHS

    • State Gov’t – California Dept of Water Resources

    • International – Finnish Met. Inst., China Heavy Rain Inst.

    • Global analysis – used by SOS

  • Wide variety

  • of data sources:


Laps features
LAPS Features

  • Modernization of traditional analysis to fully variational (var-LAPS);

  • Hot-start with cloud, vertical velocity and other meteorological states reflecting latent heat and forcing;

  • Multiscale analysis;

  • Efficient analysis for frequent analysis cycles (currently running on a single processor with 15 minute cycle for CONUS domain).

Hotstart with cloud/rain/snow

Variational LAPS has higher

ETS than persistence fcst.

What is an osse
What is an OSSE?

An OSSE is a modeling experiment used to evaluate the impact of new observing systems on operational forecasts when actual observational data is not available.

A long free model run is used as the “truth” - the Nature Run

The Nature Run fields are used to back out “synthetic observations” from all current and new observing systems.

The synthetic observations are assimilated into a different operational model

Forecasts are made with the second model and compared with the Nature Run to quantify improvements due to the new observing system

Early OSSE works confirmed data impact when observation systems have actually launched (ERS, NSCAT and AIRS, Atlas 1985,1997 and so on).

Gsd osse

UAS impact

WISDOM impact

Calibrated NOAA joint OSSE;

Report to NOAA UAS program on UAS data impact on hurricane tracks;


New nature run;

Targeting observation scheme in OSSE.

2 nd laps user workshop

Global Systems Division



Forecast Applications Branch

23-25 October 2012, Boulder, CO


Scope of workshop
SCOPE of Workshop

  • Private Sector

    • Weather Decision Tech., Hydro Meteo,

    • Precision Wind, Vaisala, Telvent

  • International agencies (10+ countries)

    • KMA, CMA, CWB, Finland (FMI), Italy, Spain,

    • BoM (Australia), Canary Islands, HKO,

    • Greece, Serbia, Nanjing Inst. of Met.

  • NOAA

    • ~120 WFOs (via AWIPS), ARL, NESDIS

  • Other US Agencies

    • DHS, DoD, FAA, CA DWR, GA Air Qual.

  • Academia

    • Univ of HI, Athens, Arizona, CIRA, UND, McGill


1 st laps workshop attendees

Oct. 25-27 2010, ESRL, Boulder, CO

2 nd laps workshop attendees

Oct. 23-25 2010, ESRL, Boulder, CO

October 23-25 2012

Workshop statistics
Workshop Statistics

Three-day workshop (Oct 23-25, 2012).


Roughly 85 attendees

23 remote attendees all US

Several countries represented (prior slide)

30 oral presentations 3-4 remote.

5 posters

Three break out topical areas (working groups)

Scientific opportunitiesfor further development

Fully variational multiscale DA approach

Dynamical constraints consistent with WRF

Use of LAPS

User feedback

Change control management

Test & evaluation

Role of NWS

Collaboration / data / software

Shared algorithm / software development

Software to digest new types of data

LAPS repository

Summary and recommendations
Summary and Recommendations

Fine scale LAPS analyses and forecasts are widely used for

Situational awareness


Continue development and support of LAPS

Serves a unique need within NWS, private sector, and internationally with 150+ total users worldwide

Unique combination of features

Fine scale, very rapid update, highly portable, easy to use

Further enhance unique features of LAPS

LAPS analysis system  was in Zoltan’s slide set ??

Introduction 2336291

Observing Systems & OSSE’s

  • Context: Observations provide the initial conditions from which model analyses and predictions are made.

  • The accuracy of our predictions primarily depend on four things:

    • Uncertainty (errors) in the initial conditions at the boundaries of our models;

    • Errors in the model’s background (or first guess) and physics;

    • Errors in the observations used to modify the initial conditions at each increment, or verify the accuracy of observations, backgrounds, analyses and predictions;

    • Errors in the analysis of these observations.

  • Bottom line: Improvements in our predictions require some effort in each of these areas.

Introduction 2336291


  • It’s difficult to verify each of these things, especially in remote regions of the planet.

  • Many of our assumptions are not well tested, especially under extreme conditions associated with severe weather and changes in planetary forcing.

  • The observations that could reduce uncertainty are expensive and the resources needed to make them are decreasing.

  • How an we insure continued forecast improvement under these circumstances?

  • This presentation describes the approach that some of us in FAB are taking.

Introduction 2336291

GPS Observations

NOAA Mission: To understand and predict changes in Earth’s environment and conserve and manage coastal and marine resources to meet our nation’s economic, social, and environmental needs

GPS-Met supports NOAA’s Mission by providing reliable and accurate refractivity & moisture estimates at low cost under all weather conditions anywhere a permanent GPS tracking station can be established.

Climate Goal:

Weather & Water Goal:

Commerce & Transportation Goal:

Satellites Modeling & Observing Systems

Introduction 2336291

TPW Uncertainty


0.24 mm  0.43 mm

Leblanc et al. 2011: Measurements of Humidity in the Atmosphere and Validation Experiments (MOHAVE)-2009: overview of campaign operations and results, Atmos. Meas. Tech., 4, 2579-2605, doi:10.5194/amt-4-2579-2011.

Introduction 2336291

Some Applications

  • Assimilation of GPS PW into operational NWP models.

  • Subjective forecasting/improved situational awareness for forecasters.

  • Quality control of in situ and remote sensing (both satellite and surface-based) moisture observations for weather, climate and research.

  • Development and testing of next-generation satellite algorithms.

Introduction 2336291

Significant Results

  • It’s possible to use a low cost total-column refractivity measurement to calibrate, validate and monitor the performance of operational observing systems

  • NOAA Global Model (GFS) has a systematic dry bias over the U.S. in summer.

  • Global models assimilating GPS observations have smaller systematic errors than models that do not.

  • Synoptic-mesoscale models assimilating GPS observations have smaller short-range RH prediction errors than models that do not.

  • Satellite microwave TPW estimates over the ocean are systematically dry-biased in the presence of clouds.

Introduction 2336291

Programmatic Issues

  • Transition of GPS Met from research into operations is stalled.

  • Funding for GPS R&D in NOAA is insufficient to support expanded use.