Thu 4/21/2016Finish Data Assimilation

• Re-cap• Ensemble Kalman filtering• A few operational examples: Tropical cyclones and DA

Course overview/synthesis• Example scenario

Reminders/announcements:• MP experiment assignment (due today)• Final presentations: 28 April, 1-4 pm (final exam period)• Complete course evaluation (online), and also survey (distributed)

Final Presentations• Project title/graphic (& update if needed) due by Monday

(so that I can advertise accordingly)

• Project presentations Thursday 4/28 – 1 week from today: Scheduled from 1 to 4 p.m. but may not take entire time

• See previous handout “assignment” for details on presentation

• Extra credit: Make video of your presentation, share on YouTube (with me, class, or everyone…) Extra credit due Monday 5/2

Re-Cap from Tuesday• What sources of information are used in DA?

• What is a key distinguishing characteristic of DA schemes?

• What is the difference between optimum interpolation and variational DA?

• What is the difference between 3DVAR and 4DVAR?

• What is ensemble Kalman filtering?

Summary of DA MethodsOptimum Interpolation, 3DVar, and Kalman Filter all have

same form, but contrast as follows:

OI: Minimize least-square error w.r.t. weight, solve sequentially; constant B

3DVAR: Form similar to OI, but derive from minimizing cost function w.r.t. analysis variables. Solve globally, can add constraints to cost function (e.g., balance), constant B

Kalman Filter: Same form, but here B not constant, solve from linear form of model (TLM) (expensive)

Ensemble Kalman Filter: Compute B from ensemble, have expense of ensemble, but don’t need TLM/adjoint model

Data Assimilation (DA) Overview (after Kalnay Fig. 5.1.2a)

Observations (+/- 3 h)

Background or first guess

Global analysis (statistical interpolation and balancing,

Quality Control [QC])

Global forecast model

Operational forecasts

6-h forecast

Initial Conditions

Approach: Use ALLavailable information for best possible analysis

Observations + background + error

statistics + dynamical and physical relations,

etc.

Re-Cap from Tuesday

Distinguishing characteristics of DA techniques: How background error is specified

• Can use “climatology” background error statistics• Or assume simple linear error growth (OI)• More advanced: Kalman filter: Use TLM to compute error growth

(with perturbation in initial conditions, for example)• Ensemble Kalman filter: Use ensemble to compute background

error

3DVAR: Limited use of observations off of analysis time

4DVAR: Use model to integrate first guess to observation time, more accurate incorporation from off-analysis times

NCEP Ensemble Skill relative to other centers

Ensemble Kalman FilterTropical Conf. talk by Dr. Greg Hakim (UW) – ca. 2010

K = Kalman gain matrix• determines relative weight on obs & background• depends on error statistics for obs & model estimate• EnKF: estimate K with an ensemble (made by perturbing observations to make ensemble of analyses)

analysis = background + weighted observations

observations - model estimate

Summary of Ensemble Kalman Filter (EnKF) Algorithm

(1) Ensemble forecast provides background estimate & statistics for new analyses

(2) Ensemble analysis with new observations

(3) Ensemble forecast to arbitrary future time

Method applied to Katrina (2005)• 90-member EnKF• WRF v2

–30-km outer grid–10-km inner grid

• observations assimilated every 6 hours.– RAOBS, surface, cloud-track winds, ACARS, NHC & RAINEX dropsondes

– no radar or satellite radiances– [10 km: NHC TC position assimilated every 3 hours]

Analysis Increment from a single dropsonde observation

of 700 hPa zonal wind

• Northward shift of storm.

Zonal Wind Increment

SN

Potential Temperature Increment

• ~thermal wind balance.

Operational DA, the GFS, and TCs

A variety of hybrid methods exist; Hamill and Snyder (2000) propose linear combination of constant B (3DVar) with one computed from ensemble

Can adjust weighting of constant versus ensemble contribution to B

This was implemented at NCEP in May 2012 for the GFS model

How are tropical cyclones represented in operational model initial conditions?

Gary LackmannNorth Carolina State University

5 July 2012

Contributions from Daryl Kleist (EMC), Mike Brennan (NHC), and John Brown (ESRL) and Briana Gordon (STI) are gratefully acknowledged

18 UTC Sunday 20 May 2012, GFS 95‐km SLP + GOES Visible

“Alberto”,   1000 mb

Outline (From NWS Webinar, 2013)A. Background and Motivation

1.) Challenges of TC prediction and initialization2.) Data Assimilation background and TC DA3.) Hybrid DA in GFS and TC IC

B. Operational Models and TC IC1.) GFS2.) HWRF3.) GFDL4.) RAP5.) NAM (briefly)

C. Conclusions

Some acronyms:TC = Tropical CycloneDA = Data AssimilationIC = Initial ConditionsEnKF = Ensemble Kalman FilterBV = Bogus Vortex

Atlantic TC track prediction: Improving

Track related to large-scale steering flow; improvements in satellite data assimilation (DA), environmental recon sampling, NWP, human forecasting skill

Intensity prediction: Slower improvement, if any

Intensity related to interaction of multi-scale processes

Source: www.nhc.noaa.gov

Track error

Intensity error

Why are intensity forecasts slow to improve?

What are challenges for numerical TC prediction?– Difficulty with initial conditions

– Need to represent complex process interactions across spatial scales (e.g., eyewall replacements; resolution)

– Difficulty representing physical TC processes (e.g., convection and swirling PBL over complex surface)

– Incomplete understanding of physical processes

TC Intensity Forecasting

Specific TC DA Challenges:1.) Sometimes not enough information, esp. inner core

- Rain contamination of some satellite-borne sensors

- Few in-situ observations other than recon

2.) Much available information is not used, esp. near TC- Obs, background can differ greatly near TC, QC eliminates obs

- Data density issues (hurricane hunter radar coverage, drops)

- Model resolution insufficient to capture inner core structure, observational representativeness challenge

TC Data Assimilation

Critical aspect: Relative weighting of observations & background (short-term model forecast) in analysis

Accurate knowledge of error associated with background and observations determines weighting

Static 3DVAR: Assume constant error statistics

Ensemble Kalman Filter (EnKF): Use ensemble to provide flow-dependent background error information

Data Assimilation

TC Initialization: GFS

GFS links to NAM, RAP, to some extent GFDL, HWRF

Starting with 12Z run, 22 May 2012, new GFS hybrid DA system implemented

Hybrid: Blend of short-term ensemble and old (constant) information to define background error

Change to Analysis from Single Observation

Single 850mb Tv observation (1K O-F, 1K error)

All ensemble error (bf-1=0.0) Hybrid, 50% ens, 50% static (bf-1=0.5)

850‐mb Tv ensemble spread, 00Z 9/12/2008

Tv observation

All static background error

Background T (contours), and change to analysis from assimilation of ob (shaded)

Slide compliments of Daryl Kleist, EMC

GFS TC InitializationInformation from: Daryl Kleist (personal communication 2012) and Kleist et al. 2011a,b

(3) NHC storm information written to “TCvitals” file; system reads location, central pressure, used in DA process regardless of 2a or 2b

(1) NHC declares storm  (TD strength or greater)

(2a) Vortex relocated to NHC position (in background field prior to DA)

Does GFS 6‐h forecast represent system?

Yes No

(2b) Synthetic (bogus) wind “observations” generated for use in DA

F06 (from 18 UTC), Valid 00 UTC 21 May 2012

Note weak representation of Bud; Tracker unable to “find” coherent system

GFSP ANALYSIS 00 UTC 21 May 2012:

Note radical change to Bud due to assimilation of synthetic wind observations (no relocation done in this case, since tracker didn’t find storm)

Example with new GFS hybrid (parallel) DA system for TS “Bud” (now operational)

Slide compliments of Daryl Kleist, EMC

GFS Parallel 5/21/12 00 UTC 850 , wind (6‐h fcst)

GFS Parallel 5/21/12  00 UTC 850 mb , wind (Analysis)

Why this paper, out of hundreds of DA papers?Understandable look at what data go into analyses

and how ICs can affect forecasts

Lucid discussion of how different types of observations are treated, and problems/procedures encountered

3DVAR system described here is very similar (same system) as used to create NARR dataset

Think about how a reanalysis changes over time, as different types of remotely sensed data were available/unavailable

Zapotocny et al. (2000)

Tables 2, 3: Errors assigned to different obs typesZapotocny et al. (2000)

Tables 7: Background Errors- A function of pressure level and variable, but not time

Zapotocny et al. (2000)

34 types of data

Many make small contribution, but when summed, make up a large fraction of total…

Taken alone, each source not that significant... “the analysis is really constructed from a very wide variety of data from a large number of sources”

Rawinsondes were still single most important source of information (at that time), but probably not any more

Zapotocny et al. (2000)

Zapotocny et al. (2000)

CONUS only

Wind observations

Figure 5: 00-h temperature sensitivity. Figure for “analysis time”, includes obs, background cycled 5 times

Data distribution by type for 00Z 12 November 2009 ) +/- 1 h)

AMDAR profiler

VAD RAOB

GPS AMV

METAR + meso

METAR by altitude

OSE: Error associated with denial of different data types

DA SummaryWe all rely on DA products more than we acknowledge!

The field of data assimilation will continue to rapidly evolve with increasing computer power

Major opportunity for advancement of NWP accuracy via EnKFand hybrid DA approaches

Potential for DA at very high resolution (e.g., 4 km, HFIP)

Students would do well to learn more about this area of major future expansion in the geosciences!

Course RetrospectiveMEA 716

Spring 2016

MEA 716: Objectives• Objectives for the Semester; Students:

– Recognize how model experiments can contribute to scientific progress, are able to utilize models to add depth and rigor to their research

– Are familiar with and able to utilize model capabilities (ability to obtain, compile, configure, and use model in scientifically meaningful ways)

– Have working knowledge of physical parameterizations available in WRF, know their limitations + interactions, and able to identify intelligent choices

– Improve student ability to use numerical models to test scientific hypotheses

– Demonstrate ability to critique model experimental design

Model usage with time (Warner, 2011, BAMS)

Model experiments and usageSubjective distinction between types of model users:

– Passive user: Work with model output from model runs of others, analyze output. Limits on depth of science.

– Active user: Run model yourself, standard namelistchanges to physics & initial conditions. Useful science can result, with limits.

– High-end user: Design experiments that require alteration of the model code or initial conditions beyond what is routinely available. Creativity and difficulty increase.

• (e.g., omission or alteration of physical processes or terrain, using data assimilation to alter initial conditions.)

– “Model developer”: Add/improve routines in model, contribute capabilities to the model that are added to the distribution, alter model code in fundamental ways

Course SummaryParameterizations are constantly changing (new ones coming

out, old ones modified)… but underlying physics of process being parameterized doesn’t change

Parameterizations cannot always be tested over entire range of parameter space; understanding what assumptions went into the schemes can help identify limitations and problems

Models have advanced remarkably in recent years. Output looks “convincing”. I hope our foray into physics parameterization has introduced some element of skepticism

Avoid perception that higher resolution is “better” – it depends on problem being studied

In this course we didn’t focus on verification/observational comparison enough

MEA 716 Philosophy• This course was about:

– Learning more about how models parameterize physical processes– Developing additional capabilities with models– Learning how to use models as research tools

• Some emphasis on physical processes, hypothesis development, how models are used as opposed to entire focus on technical aspects

• Our objectives for MEA 716:– Increase student knowledge of model physics parameterizations – Improve student ability to set up numerical-modeling based experiments,

including initial data, model domain, and choice of physics– Students gain comfort with running models as research tools (hands-on

familiarity with obtaining, compiling, configuring, and running model in meaningful ways)

– Read articles, do tests with new parameterization schemes to gain appreciation for how they work

– Also, discuss the history of NWP, provide information on data assimilation (& ensemble forecasting)

MEA 716• A few take-home points:

– Anyone can run a model, but useful research doesn’t automatically result unless thought has been put into designing meaningful experiments

– With constantly changing and new parameterization schemes, it is increasingly difficult to keep up. Don’t stop with this class!

– In this course we have barely scratched the surface in delving into the workings of model physics; I hope you feel better equipped to pick apart other aspects of a model and dig around “under the hood”

Model Usage: RecommendationsBefore even deciding to use a model, think about the problem, and clearly define scientific objective(s)

• This was the purpose of our “hypothesis development” assignment and presentation

Consider how you will use the model to achieve objectives. • Is a model even needed? • What experiments will you perform, and what output diagnostics are needed?• How will you verify that the model solution is credible?• Resist temptation to run model prematurely before carefully considering these aspects

Before selecting domain, physics choices, think about which physical processes are likely to be important

• Consider observations• Consider domain location relative to orography• What physics combination would be best suited to represent these processes?

This information will inform choices for domain location, vertical & horizontal resolution, physical parameterizations

Model Usage: Recommendations

Thoughtfully select:• parameterization schemes• domain size, lateral and horizontal boundary locations• resolution (both vertical and horizontal)• initial data source and its resolution• run duration

Consider timing of what you plan to study, allow model time to “spin up”, depending on what you’re studying (3-12 h); consider DFI option if wish to reduce

In selection of parameterizations, consider literature + knowledge of processes and scheme representation

Then… control simulation• Use observations to determine if your control simulation was useful• Examine output carefully, especially for processes you expected to be important

Model Usage: Recommendations

Set up and maintain a careful log, and organization of all model runs (e.g., “README” file in appropriate directories)

Carefully back up all needed data and software to re-run model simulations if necessary; only back up output for “production runs”

Have realistic expectations for what model can and cannot do – for some phenomena, we should not expect an very good replication of nature!

Ensembles can help to define predictability and confidence in a given model feature

Importantly: Remember that models are only one tool, and must be combined with theory and analysis to yield solid scientific results

Observations Evolving conceptual models (theory)

Dynamical models (analysis tools)

Complex Simple

(Primitive Eqn model) (analytical)

Interplay between conceptual models, observations, and dynamical models in the atmospheric sciencesAdapted from Hoskins, B. J., 1983: Dynamical processes in the atmosphere and the use of models. QJRMS 109, 1-21.