452 NWP 2015

Major Steps in the Forecast Process Data Collection Quality Control Data Assimilation Model Integration Post Processing of Model Forecasts Human Interpretation (sometimes) Product and graphics generation

Data Collection Weather is observed throughout the world and the data is distributed in real time. Many types of data and networks, including: Surface observations from many sources Radiosondes and radar profilers Fixed and drifting buoys Ship observations Aircraft observations Satellite soundings Cloud and water vapor track winds Radar and satellite imagery

Observation and Data Collection

Weather Satellites Are Now 99% of the Data Assets Used for NWP Geostationary Satellites: Imagery, soundings, cloud and water vapor winds Polar Orbiter Satellites: Imagery, soundings, many wavelengths RO (GPS) satellites Scatterometers Active radars in space (GPM)

Quality Control Automated algorithms and manual intervention to detect, correct, and remove errors in observed data. Examples: Range check Buddy check Comparison to first guess fields from previous model run Hydrostatic and vertical consistency checks for soundings. A very important issue for a forecaster--sometimes good data is rejected and vice versa.

3 March 1999: Forecast a snowstorm … got a windstorm instead Eta 48 hr SLP Forecast valid 00 UTC 3 March 1999 3 March 1999: Forecast a snowstorm … got a windstorm instead

Pacific Analysis At 4 PM 18 November 2003 Bad Observation

Forecaster Involvement A good forecast is on the lookout for NWP systems rejecting bad data, particularly in data sparse areas. Quality control systems can allow models to go off to never never land. Less of a problem today due to satellite data everywhere.

Objective Analysis/Data Assimilation Observations are scattered in three dimensions Numerical weather models are generally solved on a three-dimensional grid Need to interpolate observations to grid points and to ensure that the various fields are consistent and physically plausible (e.g., most of the atmosphere in hydrostatic and gradient wind balance).

Objective Analysis Interpolation of observational data to either a grid (most often!) or some basis function (e.g., spectral components) Typically iterative (done in several passes)

Objective Analysis/Data Assimilation Often starts with a “first guess”, usually the gridded forecast from an earlier run (frequently a run starting 6 hr earlier) This first guess is then modified by the observations. Adjustments are made to insure proper balance. Often iterative

An early objective analysis scheme is the Cressman scheme

3DVAR: 3D Variational Data Assimilation Used by the National Weather Service today for the GFS and NAM (called GSI) Tries to create an analysis that minimizes a cost function dependent on the difference between the analysis and (1) first guess and (2) observations Does this at a single time.

3DVAR Covariances: Spreads Error in Space

4DVAR: Four Dimension Variational Data Assimilation Tries to optimize analyses at MULTIPLE TIMES Tries to duplicate the observed evolution over time as well as the situation at initialization time. Uses the model itself as a data assimilation too.

4DVAR Components Full non-linear model Tangent linear version of the full model (linearized version of the forecast model) Adjoint of the tangent linear model -which allows one to integrate the model backwards. Tells sensitivity of final state to the initial state.

4DVAR Typical runs the model back and forth during an initialization period (6-12 hr), roughly ten times. Substantial computational cost. Need to have adjoint and TL version of the model. Currently used by ECMWF, CMC, UKMET, and US Navy. NOT NCEP.

Many of the next generation data assimilation approaches are ensemble based Example: the Ensemble Kalman Filter (EnKF)

Mesoscale Covariances 12 Z January 24, 2004 Camano Island Radar |V950|-qr covariance

Surface Pressure Covariance Land Ocean

An Attractive Option: EnKF Temperature observation 3DVAR EnKF

Hybrid Data Assimilation: Now Used in GFS Uses both 3DVAR and EnkF Uses EnkF covariances from GFS ensemble in 3DVAR.

Next Advance ENVAR Use temporal covariances to spread impact of observations over TIME. Will go operational NEXT WEEK. Has some of the properties of 4DVAR (adjusts model evolution)

Vertical Coordinates and Nesting

Vertical Coordinate Systems Originally p and z: but they had a problem…BC when the grid hit terrain! Then eta, sigma p and sigma z, theta Increasingly use of hybrids– e.g., sigma-theta

Sigma

Sigma-Theta

Nesting

Why Nesting? Could run a model over the whole globe, but that would require large amounts of computational resource, particularly if done at high resolution. Alternative is to only use high resolution where you need it…nesting is one approach. In nesting, a small higher resolution domain is embedded with a larger, lower-resolution domain.

Nesting Can be one-way or two way. In the future, there will be adaptive nests that will put more resolution where it is needed. And instead of rectangular grids, other shapes can be used.

Next Generation Global Models Under Development! Will use different geometries

MPAS: Hexagonal Shapes

MPAS

NOAA FIM Model

Model Integration: Numerical Weather Prediction The initialization is used as the starting point for the atmospheric simulation. Numerical models consist of the basic dynamical equations (“primitive equations”) and physical parameterizations.

“Primitive” Equations 3 Equations of Motion: Newton’s Second Law First Law of Thermodynamics Conservation of mass Perfect Gas Law Conservation of water With sufficient data for initialization and a mean to integrate these equations, numerical weather prediction is possible. Example: Newton’s Second Law: F = ma

One Form

Physics Parameterizations We need physics parameterizations to include key physical processes. Examples include radiation, cumulus convection, cloud microphysics, boundary layer physics, etc. Why? Primitive equations with lack the necessary physics or lack sufficient resolution to resolve key processes.

Parameterization Example: Cumulus Parameterization Most numerical models (grid spacing of 12-km is the best available operationally) cannot resolve convection (scales of a few km or less). In parameterization, represent the effects of sub-grid scale cumulus on the larger scales.

Numerical Weather Prediction A numerical model includes the primitive equations, physics parameterization, and a way to solve the equations (usually using finite differences on a grid) Make use of powerful computers Keep in mind that a model with a horizontal grid spacing is barely simulating phenomenon with a scale four times the grid spacing. So a 12-km model barely is getting 50 km scale features correct.

Numerical Weather Prediction Most modeling systems are run four times a day (00, 06, 12, 18 UTC), although some run twice a day (00 and 12 UTC) The main numerical modeling centers in the U.S. are: Environmental Modeling Center (EMC) at the National Centers for Environmental Prediction (NCEP)--part of the NWS. Located near Washington, DC. Fleet Numerical Meteorology and Oceanography Center (FNMOC)-Monterey, CA Air Force Weather Agency (AFWA)-Offutt AFB, Nebraska

Major U.S. Models Global Forecast System Model (GFS). Uses spectral representation rather than grids in the horizontal. Global, resolution equivalent to 13 km grid model. Run out to 384 hr, four times per day. Weather Research and Forecasting Model (WRF). Two versions: WRF-NMM and WRF-ARW(different ways of representing the dynamics). WRF is a new mesoscale modeling system system that is used by the NWS and the university/research community. AFWA also uses WRF. The NWS runs WRF-NMMB and WRF-ARW. WRF-NMMB is run at 12-km grid spacing, four times a day to 84h. Also smaller 4-km nests.

Major U.S. Models MM5 (Penn. State/NCAR Mesoscale Model Version 5). Has been the dominant model in the research community. Run here at the UW (36, 12 and 4 km resolution). COAMPS (Navy). The Navy mesoscale model..similar to MM5 There are many others--you will hear more about this in 452. Forecasters often have 6-10 different models to look at. Such diversity can provide valuable information.

Major International NWP Centers ECMWF: European Center for Medium-Range Weather Forecasting. The Gold standard. Their global model is considered the best. UK Met Office: An excellent global model similar to GFS Canadian Meteorological Center Other lesser centers

Global Forecast System (GFS) Model Previous called the Aviation (AVN) and Medium Range Forecast (MRF) models. Spectral global model and 64 levels Relatively primitive microphysics. Sophisticated surface physics and radiation Run four times a day to 384 hr (16 days!). Major increase in skill during past decades derived from using direct satellite radiance in the 3DVAR analysis scheme and other satellite assets. 13 km grid spacing equivalent over the first 10 days of the model forecast and 35 km from 10 to 16 days (384 hours). Thus, it now essential a global mesoscale model

GFS Vertical coordinates are hybrid sigma/pressure… sigma at low levels to pressure aloft.

Vertical coordinate comparison across North America

GFS Data Assimilation (GDAS) Has a later data cut-off time than the mesoscale models…and thus can get a higher percentage of data. Uses much more satellite assets..thus improve global analysis and forecasts. Major gains in southern hemisphere Hybrid Data assimilation based on 3DVAR (they call it GSI) and GFE ensemble (next slide) Every 6 hr.

GFS Hybrid Data Assimilation

GFS is not the only global model and is not the best

Higher Resolution Operational Models

Major U.S. High-Resolution Mesoscale Models (all non-hydrostatic) WRF-ARW (developed at NCAR) NMM-B (developed at NCEP Environmental Modeling Center) COAMPS (U.S. Navy) MM5 (NCAR, old, replaced by WRF) RAMS (Regional Atmospheric Modeling System, Colorado State) ARPS (Advanced Regional Prediction System): Oklahoma

Operational Mesoscale Model History in US Early: LFM, NGM (history) Eta (mainly history) MM5: Still used by some, but mainly phased out NMM- Main NWS mesoscale model, updated Eta model. Sometimes called WRF-NMM and NAM. WRF-ARW: Heavily used by research and some operational communities. NMM replaced by NMM-B

WRF and NMM

History of WRF model An attempt to create a national mesoscale prediction system to be used by both operational and research communities. A new, state-of-the-art model that has good conservation characteristics (e.g., conservation of mass) and good numerics (so not too much numerical diffusion) A model that could parallelize well on many processors and easy to modify. Plug-compatible physics to foster improvements in model physics. Designed for grid spacings of 1-10 km

Standard Physics Interface WRF Modeling System Obs Data, Analyses Post Processors, Verification WRF Software Infrastructure Dynamic Cores Mass Core NMM Core … Standard Physics Interface Physics Packages Static Initialization 3DVAR Data Assimilation

Two WRF Cores ARW (Advanced Research WRF) developed at NCAR Non-hydrostatic Numerical Model (NMM) Core developed at NCEP Both work under the WRF IO Infrastructure NMM ARW

The NCAR ARW Core Model: (See: www.wrf-model.org) Terrain following vertical coordinate two-way nesting, any ratio Conserves mass, entropy and scalars using up to 6th order spatial differencing equ for fluxes. Very good numerics, less implicit smoothing in numerics. NCAR physics package (converted from MM5 and Eta), NOAH unified land-surface model, NCEP physics adapted too

NWS NMM1—The NAM RUN Run every six hours over N. American and adjacent ocean Run to 84 hours at 12-km grid spacing. Uses the Grid-Point Statistical Interpolation (GSI) data assimilation system (3DVAR) Start with GDAS (GFS analysis) as initial first guess at t-12 hour (the start of the analysis cycle) Runs an intermittent data assimilation cycle every three hours until the initialization time. 1-Non-hydrostatic mesoscale model, NAM: North American Mesoscale run

NMM-B Hybrid sigma-pressure vertical coordinate 60 levels Betts-Miller-Janjic convective parameterization scheme Mellor-Yamada-Janji boundary layer scheme

NMM-B Details One-way nested forecasts computed concurrently with the 12-km NMM-B parent run for CONUS (4 km to 60 hours) Alaska (6 km to 60 hours) Hawaii (3 km to 60 hours) Puerto Rico (3 km to 60 hours) For fire weather, moveable 1.33-km CONUS and 1.5-km Alaska nests are also run concurrently (to 36 hours). A change in horizontal grid from Arakawa-E to Arakawa-B grid, which speeds up computations without degrading the forecast

September 2011 NAM-B Upgrade New NAM NEMS based NMMB B-grid replaces E-grid Parent remains 12 km to 84 hr Four Fixed Nests Run to 60 hr 4 km CONUS nest 6 km Alaska nest 3 km HI & PR nests Single placeable 1.33km or 1.5 km FireWeather/IMET/DHS run to 36hr

NMMB 4-km Conus

NAM Generally less skillful than GFS, even over U.S. Generally inferior to WRF-ARW at same resolution (more diffusion and smoothing, worse numerics)

Navy COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System) Sigma-Z Atmosphere And Ocean

Accessing NWP Models The department web site (go to weather loops or weather discussion) provides easy access to many model forecasts. The NCEP web site is good place to start for NWS models. http://www.nco.ncep.noaa.gov/pmb/nwprod/analysis/ The Department Regional Prediction Page gets to the department regional modeling output. http://www.atmos.washington.edu/mm5rt/

A Palette of Models Forecasters thus have a palette of model forecasts. They vary by: Region simulated Resolution Model Physics Data used in the assimilation/initialization process The diversity of models can be a very useful tool to a forecaster.

Rapid Refresh NWP The Main Tool For Nowcasting

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)

NOAA hourly updated models 13km Rapid Refresh (RAP) (mesoscale) Rapid Refresh and HRRR NOAA hourly updated models 13km Rapid Refresh (RAP) (mesoscale) Version 2 – scheduled NCEP implementation Q2 (currently 28 Jan) RAP 3km HRRR (storm-scale) HRRR High-Resolution Rapid Refresh Scheduled NCEP Implementation Q3 2014 NCEP Production Suite Review Rapid Refresh / HRRR 3-4 December 2013

RAPv2 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 Hourly Update Cycle Observations Used 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 1km Lightning (proxy reflectivity) NLDN, GLD360 Aircraft (V,T) 2-15K 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 13km GPS – Precipitable water 260 WindSat scatterometer 2-10K Partial cycle atmospheric fields – introduce GFS information 2x/day Cycle hydrometeors Fully cycle all land-sfc fields (soil temp, moisture, snow) 1-hr fcst 11 12 13 Time (UTC) Analysis Fields 3DVAR Obs Back- ground

RAPv2 Hybrid Data Assimilation 13 km RAP Cycle 13z 14z 15z ESRL/GSD RAP 2013 Uses GFS 80-member ensemble Available four times per day valid at 03z, 09z, 15z, 21z 80-member GFS EnKF Ensemble forecast valid at 15Z (9-hr fcst from 6Z) Obs Obs Obs GSI Hybrid GSI Hybrid GSI Hybrid 1 hr fcst HM Obs 1 hr fcst HM Obs HM Obs GSI HM Anx GSI HM Anx GSI HM Anx Refl Obs Refl Obs Refl Obs Digital Filter Digital Filter Digital Filter 18 hr fcst 18 hr fcst 18 hr fcst

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