# Jan Mandel/Blog/2011 Apr May

From CCM

*To the current blog page and the archive*

## April 4: Data assimilation seminar: Probability measures 1: Convergence in distribution

- Contents: definition of convergence in distribution, statement and importance of portmanteau theorem and Slutsky's theorem, continuous mapping theorem including proof. photos
- Sources:
- Slutsky's theorem from Van der Vaart's book Ch. 2, Wikipedia discussion and article, and MR1105551 ucd Dudley's paperpdf. Portmanteau theorem from Billingsley's book.
- My new notes on random variables and old notes on probability
- open courseware notes

- Purpose: For future use in continuing in our paper
*On the Convergence of the Ensemble Kalman Filter*, CCM Report 278, January 2009. arXiv:0901.2951 Slightly expanded version accepted in Applications of Mathematics

## April 6: WRF-Fire released with WRF 3.3

- The release code is based on the Nov 20, 2010 version with bug fix Jan 17, 2011 and further changes made at NCAR.

## April 9: GACR proposal

## April 11: Data assimilation seminar: Probability measures 2: Proof of portmanteau theorem

## April 18: Data assimilation seminar: Probability measures 3: Proof of Slutsky's theorem

- This is the third part of the series of convergence theorems in probability to use in ongoing work on data assimilation in infinite dimensional spaces. This theory is generally presented in finite dimension in the literature. We are going over the proofs in detail and seek suitable variants to make sure that they still hold.
- photos

## April 20: Fireflux paper

## April 25: Data assimilation seminar: Probability measures 4: Uniform integrability

Vitalli convergence theorem assumes only uniform integrability, which is weaker than the inequality assumed in the dominated convergence theorem. We show some interesting relations between uniform integrability, convergence in probability, and in Lp. This is the fourth part of the series of convergence theorems in probability to use in ongoing work on data assimilation in infinite dimensional spaces. This theory is generally presented in finite dimension in the literature. We are going over the proofs in detail and seek suitable variants to make sure that they still hold for random elements with values in Banach spaces.

## April 25: Fuqing Zhang: Inter-Comparison and Coupling of EnKF with 4DVAR

## April 26: WRF For Hurricanes Tutorial

### Rob Rogers: Observations of hurricanes to improve numerical models

- model evaluation, data assimilation, hypothesis testing (not in statistical sense?)
- observations need to be in compatible format with models
- airborne: expendables, remote sensors
- transmitted from aircraft - doppler real time SOs trenamitted during P-3 mission and assimilated into HWRFx using HEDAS
- HEDAS
- impact of inner core observations: doppler data improves intensity error up to 72 hours but not later, frequency of superior forecast: better after 84 hours
- contact

### Fuqing Zhang: Assimilation of fine-scale hurricane observations

- EnKF Meng and Zhang 2008a,b; data assimilated WSR88D at KCRP, KHGX,, KLCH radar radial velocity; superobservations, WRF-EnKF 3 domain, 40.5-4.5 km, 60 member ensemble
- Katrina: WRF ARW 3.1 pertubations by WRFDA; EnKF_DF closely followed the best track except 6h delay; after the 2nd cycle EnKF analyses reproduce the observed with structure quite well; the differences are small with more than 60 members (tried 200, 300); most of the errors come from wave numbers 0, 1, maybe 2, 60 members capture that well; for the wavenumber 1-2 example the magnitudes of covariance are similar but in different locations; skill decay fig (how defines skill?)
- Penn state WRF-EnKF realtime system
- pseudo-ensemble hybrid data assimilation system (PEDA) for TC initialization with airborne doppler radar data
- figure: max windspeed error remains about constant, WVAR,PEDA best
- figure: abs error in position statrts at cca 20 -> 300km at 72 hours, keeps increasing NoDA best, WVAR PEDA worst
- diagram: GFS analysis, very good; 60 ensemble pertiubations generated from B or WRF-Var; replace TC vortex; pseudo ensemble members; flow-dependent inner core; 3DVAR
- TC vortex libary: TC vortices at different output times are binned according to vma
- fig: errors after bias correction (NHC variable interpolator)
- need 16000 cores for 1.5km ensemble in 6 hours (TACC Ranger)

### Chris Snyder: Mesoscale Data Assimilation for Hurricanes

- observations - relevant for environment (jm: additive), relevant for vortex (jm: positional) - sat images and recon flights, cloud-track winds, special dropsondes, doppler radar
- observations are limited an intermittent, do not resolve all aspects of vortex structure or evolution
- bogussing - assumed vortex structure; vortex removal and reloacation - (jm: much more than just deformation): extract from model forecasest thhourh spatial filter GFDL technique, spin up symmetric vortex, estimate asymmetry, insert the vortex
- data ssimilation (DA): simplest: strong assumptions about prior covariance - independent in time, depends on distance only, no dependence on state. Sophisticated: relax assumptions, incorporate info
- differences between EnKF and 4DVAR apparent when observations are incomplete
- simple example vortex=maximum, single observation of vortex. 3d var puts in a bump (corrupts vortex structure), EnKF shifts the vortex coherently (jm: only if there is a suitable member, that's why they need so many), prerseves structure
- analysis from WRF/DART: 96 members GFS 6hour forecast+spatially correlated noise for lateral boundary conditions; run continuously for 4 months, just using data, no artificial intervention (for position); crucial to include flow dependent covariance via model dynamics (fig: moved vortex position, two color blotches with wind difference caused by the move)

## April 26: Revisiting the EnKF theory paper

## April 27: WRF-Fire

- photos: log profile, data assimilation, branding

## April 30: Reply to referee 1 for the GMD paper

## May 1: Whitepaper on wildland fire modeling

- pdf sources references
- http://whitepapertemplate.net
- http://home.earthlink.net/~jeninger/NWDSWhitePaper.pdf

## May 2: CCM Colloquium: Volodymyr Kondratenko

**Ignition from a Fire Perimeter in WRF Wildland Fire Model**

- Abstract: The current WRF-Fire model starts the fire from a given ignition point at a given time. We want to start the model from a given fire perimeter at a given time instead. However the fuel balance and the state of the atmosphere depend on the history of the fire. The purpose of this work is to create an approximate artificial history of the fire based on the given fire perimeter and time and an approximate ignition point and time. Replaying the fire history then establishes a reasonable fuel balance and outputs heat fluxes into the atmospheric model that allow the atmospheric circulation to develop. Then the coupled atmosphere-fire model takes over. In this preliminary investigation, the ignition times in the fire area is calculated based on the distance from the ignition point to the perimeter, assuming that the perimeter is convex or star-shaped. Simulation results for an ideal example show that the fire can continue in a natural way from the perimeter. Possible extensions include algorithms for more general perimeters and running the fire model backwards in time from the perimeter to create a more realistic history.
- Uses the fire replay feature in WRF-Fire, implemented earlier

## May 2: Data assimilation seminar

### Convergence of the EnKF revisited

- We'll revisit the proofs in our soon-to-be-published 2009 paper arXiv:0901.2951
- pdf source revised arXiv sources
- photos

### EnKF Matlab classes for hurricane test

## May 3: Revised EnKF theory paper submitted

## May 4: Revising Harmanli fire paper

## May 9:Data assimilation seminar: Bryan Smith: Statistics on manifolds 2

## May 9: Wildfires project coordination meeting at the SCI Institute

## May 10: Wildfires meeting at University of Utah Meteorology

## May 11: Convergence study of the level set method

- Posted as interactive comment to our GMDD paper in response to reviewers. Published as Geoscientific Model Development Discussions 4, 497, C226 in the pdf supplement
- Also posted as CCM Report 300

## May 12: NASA ROSES 2011

- NASA main page solicitation local copy pdf ROSES 2011 Clarifications, corrections and amendments due dates
- A.35 FIRES solicitation local copy pdf
- A.41 AIST AIST program page

## May 13: WRF-Fire

- WRF info
- WRF 3.3 release
- Summary of changes at NCAR after we have submitted the WRF-Fire code for the release
- branch wrfv3.3_svn: mirrored relevant commits from the WRF repository.
- WRF-Fire mailing list post. However, the only change of substance was removing the wind reduction factors in this commit on Mar 4 2011.
- Our paper describing the code submitted for the release and a comment in the interactive discussion noting that the release may be different.

## May 14: Abstracts for 9th Symposium on Fire and Forest Meteorology, AMS

## May 17: Data assimilation seminar: Osimorph Matlab classes

## May 30: at CAS

### WRF workshop perimeter ignition paper

- skeleton, intro, conclusion done paper source files pdf
- Volodymyr's pictures showing that the difference is small
- earlier: colloquium May 2 implementation of the replay