1. The NCAR/ATEC Real-Time Four-Dimensional Data Assimilation and Forecast System (RTFDDA) Yubao Liu, Laurie Carson, Francois Vandenberghe Chris Davis, Mei Xu, Rong Sheu, Al Bourgeios, Fei Chen and Daran Rife Project Leaders: Scott Swerdlin and Tom Warner  Problems, solutions and goals  Scientific design  Engineering aspects  Hardcore issues: experience  On-going developments

2. Issues for Mesoscale Analysis and Forecast Data are sparse and irregular in space and time and they are not sufficient to describe the structures of local-scale circulations. Local circulations are complicated – Large-scale forcing and multiscale interaction – Local terrain forcing – Contrasts in surface heating/cooling – Land-soil moisture and thermal properties A full-physics mesoscale model with accurate local forcing + use of all data.

3. NCAR/ATEC RTFDDA Is Such a System PSU/NCAR MM5 (version 3.6) based, Real-time and Relocatable, Multi-scale: meso-   meso-  x = 0.5 – 45 km  Rapid-Cycling: at a flexible interval of 1 – 12 hours, FDDA: 4-D continuous data assimilation, and Forecast ( 0 – 48 hours) systems. Main Objective: effectively combines the full-physics MM5 model with all available observations to produce best-possible real-time local-scale analyses and 0 – 48 hour forecasts

4. Data Assimilation and Forecasting FDDA is based on “Observation-Nudging” Technique  Stauffer and Seaman (1995), and  Numerous modifications and refinements by NCAR/ATEC modelers. ( See ~20 pubs at https://4dwx.org/publications/ ) FDDA is based on “Observation-Nudging” Technique  Stauffer and Seaman (1995), and  Numerous modifications and refinements by NCAR/ATEC modelers. ( See ~20 pubs at https://4dwx.org/publications/ ) Cold Start t Forecasts (MM5/WRF) FDDA (MM5) Observations (synoptic/asynoptic) (once a week)

5. Obs-nudging: Weighting Functions W = W qf W horizontal W vertical W time

6. Obs-nudging: Weighting Functions Weighting functions should depend on grid sizes; local terrain; observation location, time, quality, platforms; and air stream properties. W = W qf W horizontal W vertical W time OBS Hi sfc

7. Advantages of Continuous OBS-Nudging  Allows for model-defined solution in data-sparse regions, but adjusts for observations where they exist.  Combines the dynamic balance and physical forcing of a model, with observation information available at and before forecast time.  Provide 4-D, continuous, “spun-up” and complete analyses and I.C. for nowcasts/forecasts: –Local circulations and cloud and precipitation fields Note: “Analysis Nudging” technique may not be applicable in meso-  and  scale models

8. Operational RTFDDA Systems Regular Operational RTFDDA Systems Oct.10,00 June.1,02 Feb.5,02 Jul.2,01 Sep.4,01 5 permanent + 8 short-term systems Special-operation Sites Afghanistan Athens-2004 Iraq

9. RTFDDA Engineering Design MACs and DACs MACs: 16 – 48-node linux clusters 1 - 3 GHz dual-CPUs with Myrinet networking Parallel data collection, data assimilation and post- processing Graphics and file service of various formats Archiving, verification and local-scale climotologies Tools for system monitoring and recovery

10. Domain Configuration Example (130 km x 85 km) Grid 1: 36km Grid 2: 12km Grid 3: 4.0km Grid 4: 1.33 km 2002 SLC Olympics

11. Diverse and Frequent Observations (An example)

12. U (m/s) Model vs Observation (750–300hPa) Valid at 00 UTC of the 5- day simulation Soundings (1665) Profilers (3717) ACARS (4220) SATWINDs (2323) obs model BIAS = 0.6 RMSE=3.1 BIAS = 1.1 RMSE=3.3 BIAS = 0.5 RMSE=2.1 BIAS = 1.1 RMSE=4.1

13. RT-FDDA Model System GMOD  GUI Ensemble  Anal/Fcst WRF FDDA Model physics Data assimilation schemes SST, snow cover/depth, sea ice LSM DA for soil properties GPS Sat Tb … QC METAR, spec, buoy, ship, temp, pilot, speci, Mesowest, Satellite winds, ACARS, NPN profilers, CAP profilers, radar data, range SAMS, soundings and profilers, cloud / precipitation, and … Forecasts RTFDDA Analyses More data; No bad data; Use of data quality Dealing with the model errors – physics bias Fine-tuning data assimilation weighting function Considering small-scale uncertainties RUC ETA..

14. GMOD (Global Meteorology on Demand)

15. Summary The goal of the RTFDDA system is to produce local- scale four-dimensional analyses and forecasts for various weather-critical applications, tests and events. The RTFDDA system has proven to be reliable, reasonably accurate, and widely applicable. The operational RTFDDA systems have become a dependable tool for our users. Continuous enhancements are being made to improve each system component, including data handling, data assimilation schemes, model physics…

16. Weather Systems for Regional NWP Encompass several meteorological scales: –Synoptic ~ 1000 km High/low pressure systems, fronts, cyclones … Need 20 – 30-km grids –Mesoscale phenomena ~ 5 – 100 km Mountain/valley circulations, sea breezes, convective systems, urban effects … Need 0.5 – 10-km grids

17. The end. Thank you!

18. Scientific Issues for mesoscale prediction Predictability-limits favor shorter forecasts Strong dependence on initial conditions (IC) “Spin-up” of dynamics and cloud/precipitation Sparse and uneven observations Dependency on larger scale models through boundary conditions (BC) Model physics are extremely important

19. Solution and Goals Real-time observations Collect as many observations as possible Full use of the observations Conduct dynamic and cloud/precipitation initialization to eliminate/mitigate the “spin-up” problem, Aim for best-possible “CURRENT” analyses and 0 – 12h forecasts, Predictability is good, Analysis/observations are effective, Large scale model (B.C.) is accurate, Fill the “Gaps” of the national operational center models. and accurate 12 – 48 hour forecasts.