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) 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: 3

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). Variational LAPS has higher ETS than persistence fcst. Hotstart with cloud/rain/snow

OSSE Endeavors

What is an OSSE? 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). 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.

GSD OSSE Calibrated NOAA joint OSSE; Report to NOAA UAS program on UAS data impact on hurricane tracks; WISDOM OSSE; New nature run; Targeting observation scheme in OSSE. UAS impact WISDOM impact

LAPS Workshop

2 ND LAPS USER WORKSHOP 9 Global Systems Division NOAA/OAR/ESRL Acknowledgements: Forecast Applications Branch October 2012, Boulder, CO

10 SCOPE of Workshop 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 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.

1 ST LAPS WORKSHOP ATTENDEES Oct , ESRL, Boulder, CO

2 nd LAPS WORKSHOP ATTENDEES Oct , ESRL, Boulder, CO October

Workshop Statistics Three-day workshop (Oct 23-25, 2012). Attendees – 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 opportunities for 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 Fine scale LAPS analyses and forecasts are widely used for – Situational awareness – Warn-On-Forecasting – 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 ??

LAPS Observing Systems

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: 1)Uncertainty (errors) in the initial conditions at the boundaries of our models; 2)Errors in the model’s background (or first guess) and physics; 3)Errors in the observations used to modify the initial conditions at each increment, or verify the accuracy of observations, backgrounds, analyses and predictions; 4)Errors in the analysis of these observations. Bottom line: Improvements in our predictions require some effort in each of these areas.

Issues 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.

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 Climate Goal: Weather & Water Goal: Commerce & Transportation Goal: Satellites Modeling & Observing Systems 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.

Techniques

MOHAVE 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, , doi: /amt TPW Uncertainty

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.

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.

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.