Rapid Refresh and RTMA RUC AKARapid Refresh A

Rapid Refresh and RTMA

RUC: AKA-Rapid Refresh • 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 short-term forecasts (1 -3 -6 hr) • The RUC/RR ingests and assimilates data hourly, and then makes short-term forecasts • Uses the WRF model…which uses a hybrid sigma/isentropic vertical coordinate • Resolution: Rapid Refresh: 13 km and 50 levels, High Resolution Rapid Refresh (3 km)

Rapid Refresh and HRRR NOAA hourly updated models 13 km Rapid Refresh (RAP) (mesoscale) Version 2 – scheduled NCEP implementation Q 2 (currently 28 Jan) 3 km HRRR (storm-scale) High-Resolution Rapid Refresh RAP HRRR Scheduled NCEP Implementation Q 3 2014 NCEP Production Suite Review Rapid Refresh / HRRR 3 -4 December 2013 4

RAPv 2 Prediction System Overview • Hourly updated mesoscale analyses / forecasts • WRF-ARW model (Grell-3 cumulus param, Thompson microphysics, RUC-Smirnova land-surface, MYNN PBL scheme) • GSI hybrid analysis using 80 -member global ensemble • 13 -km, 50 levels, 24 cycles/day – each run out to 18 hours • 6 -hour catch-up “partial” cycle run twice per day from GFS • Output grids: 13, 20, and 40 km CONUS, 32 km full domain, 11 km Alaska, 16 km Puerto Rico • Use and downstream dependencies • Used by SPC, AWC, WPC, NWS FOs, FAA, energy industry, and others for short-range forecasts and hourly analyses • Downscaled RAP serves as first guess for RTMA • RAP serves as initial condition for SREF members • RAP will be used to initialize Hi-Res Rapid Refresh (HRRR)

Rapid Refresh Observations Used Hourly Update Cycle Partial cycle atmospheric fields – introduce GFS information 2 x/day Cycle hydrometeors Fully cycle all land-sfc fields (soil temp, moisture, snow) 1 -hr fcst Back- Analysi groun s d Fields 3 DVA Fields R R Obs 11 12 13 Time (UTC) Hourly Observations RAP 2013 N. Amer Rawinsonde (T, V, RH) 120 Profiler – NOAA Network (V) 21 Profiler – 915 MHz (V, Tv) 25 Radar – VAD (V) 125 Radar reflectivity - CONUS 1 km Lightning (proxy reflectivity) NLDN, GLD 360 Aircraft (V, T) 2 -15 K Aircraft - WVSS (RH) 0 -800 Surface/METAR (T, Td, V, ps, cloud, vis, wx) 2200 - 2500 Buoys/ships (V, ps) 200 -400 GOES AMVs (V) 2000 - 4000 AMSU/HIRS/MHS radiances Used GOES cloud-top press/temp 13 km GPS – Precipitable water 260

RAPv 2 Hybrid Data Assimilation 14 z 13 z ESRL/GSD RAP 2013 Uses GFS 80 -member ensemble Available four times per day valid at 03 z, 09 z, 15 z, 21 z HM Obs Refl Obs GSI Hybrid GSI HM Anx Digital Filter Obs 1 h r fc st Obs HM Obs Refl Obs 18 hr fcst GSI Hybrid GSI HM Anx Digital Filter 15 z 80 -member GFS En. KF Ensemble forecast valid at 15 Z (9 -hr fcst from 6 Z) Obs 1 h r fc st 13 km RAP Cycle HM Obs Refl Obs 18 hr fcst GSI Hybrid GSI HM Anx Digital Filter 18 hr fcst

RUC History – NCEP (NMC) implementations 1994 - First operational implementation of RUC - 60 km resolution, 3 -h cycle 1998 – 40 km resolution, 1 -h cycle, - cloud physics, land-sfc model 2002 – 20 km resolution - addition of GOES cloud data in assimilation 2003 – Change to 3 d. VAR analysis from previous OI (April) 2004 – Vertical advection, land use (April) PBL-depth for surface assimilation (September) 2005 – 13 km resolution, new obs, new model physics (June) 2011 – WRF-based Rapid Refresh w/ GSI to replace RUC

Rapid Refresh: 13 km and 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.

Real-Time Mesoscale Analysis RTMA • Downscales a short-term forecast to fineresolution terrain and coastlines and then uses observations to produce a fine-resolution analysis. • Performs a 2 -dimensional variational analysis (2 d -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 1 hour 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.

TX 2 m Temperature Analysis 23