Model Diagnostics HRD Activities and Plans HFIP Diagnostics

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Model Diagnostics: HRD Activities and Plans HFIP Diagnostics Workshop Friday, August 10, 2012 Joe

Model Diagnostics: HRD Activities and Plans HFIP Diagnostics Workshop Friday, August 10, 2012 Joe Cione, Rob Rogers, Eric Uhlhorn, Tomi Vukicevic, and Jun Zhang

Outline • HWRF surface-layer evaluations (Cione, Uhlhorn) • Preliminary HWRF vortex- and convective-scale evaluations

Outline • HWRF surface-layer evaluations (Cione, Uhlhorn) • Preliminary HWRF vortex- and convective-scale evaluations during RI (Rogers) • Preliminary HWRF PBL evaluations (J. Zhang) • New low-wavenumber verification metric (Vukicevic)

What is the dominant thermodynamic factor impacting TC surface moisture flux? Ocean (qs(SST))…or…Atmosphere (q

What is the dominant thermodynamic factor impacting TC surface moisture flux? Ocean (qs(SST))…or…Atmosphere (q 10) TCBD (observations) HWRF V 3. 2

How well does HWRF represent low-order structure of an intensifying TC? • Composites of

How well does HWRF represent low-order structure of an intensifying TC? • Composites of inner-core structure from airborne Doppler for intensifying and steady-state TC’s indicate distinct differences in inner-core structure • How well do HWRF forecasts of intensifying and steady-state TC’s depict this structure? Can it capture these differences? • First step: start with a single HWRF forecast of an intensifying TC (Earl 2010) 3 -km HWRF (initialized 12 UTC 27 August) forecast track and intensity

Intensifying (I) Intensity traces HWRF Earl forecast Steady-state (SS)

Intensifying (I) Intensity traces HWRF Earl forecast Steady-state (SS)

I height (km) 14 13 12 11 10 9 8 7 6 5 4

I height (km) 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 0. 5 1 1. 5 2 2. 5 3 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 SS 0. 5 1 1. 5 2 r/RMW 14 13 12 11 10 9 8 7 6 5 4 3 2 1 0 HWRF 0. 5 1 1. 5 r/RMW 33 30 27 24 21 18 12 9 6 5 4 3 r/RMW height (km) Axisymmetric vertical vorticity (x 10 -4 s-1) 2 2. 5 3 33 30 27 24 21 18 12 9 6 5 4 3 2. 5 3

Locations of convective bursts # of burst grid points (> 5. 5 m/s) =

Locations of convective bursts # of burst grid points (> 5. 5 m/s) = 749 # of burst grid points (> 5. 5 m/s) = 342 I SS # of burst grid points (> 3 m/s) = 300 HWRF

Radial distribution of convective bursts SS frequency (%) I r/RMW frequency (%) HWRF r/RMW

Radial distribution of convective bursts SS frequency (%) I r/RMW frequency (%) HWRF r/RMW

HFIP proposal PI: Jun Zhang, David Nolan and Sylvie lorsolo Collaborators: Robert Rogers and

HFIP proposal PI: Jun Zhang, David Nolan and Sylvie lorsolo Collaborators: Robert Rogers and Paul Reasor Objectives: To develop metrics for evaluating the inner-core structure simulated by the HWRF model in order to improve the intensity and track forecast. 1) vortex-scale structure 2) convective-scale structure 3) boundary layer structure Year 1: 1) develop metrics for model evaluation through analyzing observational data (i. e. , Doppler radar and dropsonde data) 2). Test the structural metrics using case study 9

Height of Vtmax model Black dashed line represents the height of maximum tangential wind

Height of Vtmax model Black dashed line represents the height of maximum tangential wind speed 10

Inflow layer depth model Black line represents the height of 10% peak inflow 11

Inflow layer depth model Black line represents the height of 10% peak inflow 11

Mixed layer depth model Black line represents dθv/dz=3 K/km 12

Mixed layer depth model Black line represents dθv/dz=3 K/km 12

Future work Test other metrics using the Earl run: 1. eyewall slope 2. TKE

Future work Test other metrics using the Earl run: 1. eyewall slope 2. TKE 3. CFADs of vertical velocity 4. Metrics for asymmetric structure …… 13

A new metric for intensity forecast verification Tomi Vukicevic, Erick Uhlhorn, Paul Reasor and

A new metric for intensity forecast verification Tomi Vukicevic, Erick Uhlhorn, Paul Reasor and Brad Klotz NOAA/AOML/HRD

Maximum intensity decomposition • Without approximation, the maximum intensity of a wind field at

Maximum intensity decomposition • Without approximation, the maximum intensity of a wind field at 10 m (or any height) in coordinate system could be expressed by the following decomposition and are amplitudes of azimuthal wave number 0 and 1, respectively • is total contribution from higher harmonics • • Maximum intensity = deterministic + stochastic component • By definition because

Maximum intensity verification using the decomposition • is characterized by a non-Gaussian pdf •

Maximum intensity verification using the decomposition • is characterized by a non-Gaussian pdf • Verification of the maximum intensity consists of – Verification of PDF and – Deterministic amplitude • Data used for verification – Observations: BT, SFMR (P-3/NOAA and AF), TDR (P-3) Model : HWRFx forecasts and HEDAS analyses • Cases from 2008 -2011 seasons

Verification of stochastic component : comparison of HEDAS V 0, V 1 and Vmax

Verification of stochastic component : comparison of HEDAS V 0, V 1 and Vmax FORECAST V 0, V 1 and Vmax All PDFs show stochastic nature of with small mean, standard deviation compatible with the BT error estimate (~4 -5 m/s), and positive skewness Forecast- and observation-based PDFs are compatible implying that the forecast has good skill for the stochastic component of the intensity PDFs HEDAS V 0 and V 1 USING BT Vmax OBSERVATIONS ONLY SFMR V 0+V 1 and BT Vmax OBSERVATIONS ONLY TDR V 0 and V 1 and BT Vmax

Verification of deterministic component • So far have forecast decomposition results Examples of Wave

Verification of deterministic component • So far have forecast decomposition results Examples of Wave 0 RI, while stochastic component is negative • Working on verification against SFMR based decomposition – Need to match SFMR and Forecast dates for verification of wave 0 and 1 amplitudes