Transcript WRF Model Family

Rapid Update Cycle-RUC
Rapid Refresh-RR
High Resolution Rapid RefreshHRRR
RTMA
http://rapidrefresh.noaa.gov/
Rapid Update Cycle: RUC
(replaced last year)
• A major issue is how to assimilate and use the
rapidly increasing array of off-time or continuous
observations (not a 00 and 12 UTC world
anymore!)
• Want very good analyses and very good shortterm forecasts (1-3-6 hr)
• The RUC ingested and assimilated data hourly,
and then makes short-term forecasts
• Used the MAPS mesoscale model…which used a
hybrid sigma/isentropic vertical coordinate
• Resolution: 13 km and 50 levels
Domain
13km RUC
Terrain elevation - 100 m interval
Observations used in RUC
Data Type
~Number
Freq.
-------------------------------------------------Rawinsonde
80
/12h
NOAA profilers
30
/ 1h
VAD winds
110-130
/ 1h
Aircraft (V,temp)
1400-4500 / 1h
Surface/METAR
1500-1700 / 1h
Buoy/ship
100-150
/ 1h
GOES precip water 1500-3000 / 1h
GOES cloud winds
1000-2500 / 1h
GOES cloud-top pres 10 km res / 1h
SSM/I precip water 1000-4000 / 6h
-------------------------------------------------GPS precip water
~300
/ 1h
Mesonet
~5000
/ 1h
METAR-cloud-vis-wx ~1500
/ 1h
--------------------------------------------------
RUC History – NCEP (NMC) implementations
1994 - First operational implementation of RUC
- 60km resolution, 3-h cycle
1998 – 40km resolution, 1-h cycle,
- cloud physics, land-sfc model
2002 – 20km resolution
- addition of GOES cloud data in assimilation
2003 – Change to 3dVAR analysis from previous OI
(April)
2004 – Vertical advection, land use (April)
PBL-depth for surface assimilation (September)
2005 – 13km resolution, new obs, new model physics
(June)
2012 – WRF-based Rapid Refresh w/ GSI replacedRUC
Rapid Refresh
• A new version of RUC has been developed,
that uses the WRF model instead of the
MAPS model. Called the Rapid Refresh.
• Larger domain
Rapid Refresh: 13 km but larger
domain
High-Resolution Rapid Refresh:
3 km, 1 hr, smaller domain
RTMA
(Real Time Mesoscale Analysis System)
NWS New Mesoscale Analysis
System for verifying model output
and human forecasts.
RTMA
• Downscales a short-term forecast to fine-resolution
terrain and coastlines and then uses observations to
produce a fine-resolution analysis.
• Performs a 2-dimensional variational analysis (2d-var)
using current surface observations, including mesonets,
and scatterometer winds over water, using short-term
forecast as first guess.
• Provides estimates of the spatially-varying magnitude
of analysis errors
• Also includes hourly Stage II precipitation estimates
and Effective Cloud Amount, a GOES derived product
• Either a 5-km or 2.5 km analysis.
RTMA
• The RTMA depends on a short-term model
forecast for a first guess, thus the RTMA is
affected by the quality of the model's
analysis/forecast system
• CONUS first guess is downscaled from a 1hour RR forecast.
• Because the RTMA uses mesonet data, which
is of highly variable quality due to variations
in sensor siting and sensor maintenance,
observation quality control strongly affects the
analysis.
Why does NWS want this?
• Gridded verification of their gridded
forecasts (NDFD)
• Serve as a mesoscale Analysis of Record
(AOR)
• For mesoscale forecasting and studies.
RTMA 2dVAR update
RUC-RTMA downscaling
26
to detailed RTMA background
RTMA 2dVAR update
RUC-RTMA downscaling
27
to detailed RTMA background
TX 2 m Temperature Analysis
28
TX 2 m Temperature Analysis
29
TX 2 m Temperature Increment
30