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

13km Rapid Refresh (RAP) (mesoscale) Version 2 – scheduled NCEP implementation Q2 (currently 28 Jan) 3km HRRR (storm-scale) High-Resolution Rapid Refresh Scheduled NCEP Implementation Q3 2014 RAP HRRR

NCEP Production Suite Review Rapid Refresh / HRRR 3-4 December 2013

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RAPv2 Prediction System Overview

• • • • • •

Hourly updated mesoscale analyses / forecasts

•

WRF-ARW model scheme) (Grell-3 cumulus param, Thompson microphysics, RUC-Smirnova land-surface, MYNN PBL 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 Hourly Update Cycle Observations Used Partial cycle atmospheric fields – introduce GFS information 2x/day Cycle hydrometeors Fully cycle all land-sfc fields (soil temp, moisture, snow) Hourly Observations

Rawinsonde (T,V,RH) Profiler – NOAA Network (V)

1-hr fcst 1-hr fcst Back groun d Fields R Analysi s Fields Obs 3DVA R Obs 1-hr fcst 11 12 13

Profiler – 915 MHz (V, Tv) Radar – VAD (V) Radar reflectivity - CONUS

Lightning (proxy reflectivity) Time (UTC)

Aircraft (V,T) Aircraft - WVSS (RH) Surface/METAR (T,Td,V,ps,cloud, vis, wx) Buoys/ships (V, ps) GOES AMVs (V) AMSU/HIRS/MHS radiances GOES cloud-top press/temp GPS – Precipitable water WindSat scatterometer

RAP 2013 N. Amer

120 21 25 125 1km

NLDN, GLD360

2-15K 0-800 2200- 2500 200-400 2000- 4000 Used 13km 260 2-10K

RAPv2 Hybrid Data Assimilation

13 km RAP Cycle 13z ESRL/GSD RAP 2013 Uses GFS 80-member ensemble Available four times per day valid at 03z, 09z, 15z, 21z 14z 15z 80-member GFS EnKF Ensemble forecast valid at 15Z (9-hr fcst from 6Z) GSI Hybrid GSI Hybrid GSI Hybrid GSI HM Anx GSI HM Anx GSI HM Anx Digital Filter 18 hr fcst Digital Filter 18 hr fcst Digital Filter 18 hr fcst

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) 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 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 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

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