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Transcript of The NCAR/ATEC Real-Time Four-Dimensional Data Assimilation and Forecast System (RTFDDA) Yubao Liu,...
The NCAR/ATEC Real-Time Four-Dimensional Data Assimilation and Forecast System (RTFDDA)
Yubao Liu, Laurie Carson, Francois VandenbergheChris 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
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.
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
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
Data Assimilation and Forecasting
FDDA is based on “Observation-Nudging” TechniqueStauffer 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” TechniqueStauffer 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)
Obs-nudging: Weighting Functions
W = Wqf Whorizontal Wvertical Wtime
Obs-nudging: Weighting Functions
Weighting functions should depend on grid sizes; local terrain; observation location, time, quality, platforms; and air stream properties.
W = Wqf Whorizontal Wvertical Wtime W = Wqf Whorizontal Wvertical Wtime
OBS
OBS
Hi
sfc
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
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-2004Iraq
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
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
Diverse and Frequent Observations(An example)
U (m/s)
Modelvs
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
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 …
ForecastsRTFDDA Analyses
More data; No bad data; Use of data quality Dealing with the model errors – physics biasFine-tuning data assimilation weighting functionConsidering small-scale uncertainties
RUC
ETA..
GMOD (Global Meteorology on Demand)
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…
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
The end. Thank you!
Scientific Issues for mesoscale prediction
Predictability-limits favor shorter forecastsStrong dependence on initial conditions (IC)
“Spin-up” of dynamics and cloud/precipitation Sparse and uneven observationsDependency on larger scale models through
boundary conditions (BC)Model physics are extremely important
Solution and GoalsReal-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.