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NESDIS Contributions to the Hurricane Forecast Improvement Program (HFIP). Mark DeMaria* NOAA/NESDIS/RAMMB HFIP Planning Meeting Silver Spring, MD, 23-24 October 2008. * With contributions from B. Pichel, P. Chang, Z. Jelenak, L. Miller, F. Weng, J. Knaff (NESDIS)

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Nesdis contributions to the hurricane forecast improvement program hfip

NESDIS Contributions to the Hurricane Forecast Improvement Program (HFIP)

Mark DeMaria*

NOAA/NESDIS/RAMMB

HFIP Planning Meeting

Silver Spring, MD,

23-24 October 2008

*With contributions from B. Pichel, P. Chang, Z. Jelenak, L. Miller, F. Weng, J. Knaff (NESDIS)

B. McNoldy, K. Maclay, I. Jankov, A. Schumacher, R. Brummer (CIRA)


Outline
Outline Program (HFIP)

  • Sounders and Imagers

    • Coordination with JCSDA on assimilation

  • Ocean surface winds

  • Surface wind information content from satellites

    • Use of NESDIS TC surface wind products for model evaluation

  • Additional surface and oceanic information

    • Synthetic Aperture Radar (SAR) and satellite altimetry

  • Model verification

    • Evaluation in satellite observation space

    • Evaluation in the context of statistical models

  • Forecast impact assessment with NHC’s wind probability model



Satellite data for hurricane models
Satellite Data for Hurricane Models Program (HFIP)

  • Sounders (T, q, SST, land surface properties)

    • Polar

      • Microwave and IR

    • Geostationary

      • IR

  • Imagers

    • Feature track winds

    • Cloud properties

    • SST

    • Cloud/precip structure below cloud top from microwave imagers

    • Land surface properties

  • Scatterometers

    • Ocean surface winds

  • Satellite altimeters

    • Sub-surface ocean structure

  • Lightning mapper (planned for GOES-R)


Operational sounders
Operational Sounders Program (HFIP)

  • GOES

    • 20 channel IR, 10 km spatial, 1 hr temporal

  • NOAA POES

    • -wave (AMSU), 15 T, 5 q channels

      • 50 km spatial, 12 hr temporal

    • IR (HIRS), 20 channels

      • 20 km spatial, 12 hr temporal

  • Met-Op

    • AMSU + hyperspectral IR (~8000 channels)

    • 50/12 km spatial, 12 hr temporal

  • DMSP

    • SSM/T µ-wave sounder

  • NPOESS (~2015)

    • Advanced microwave + hyperspectral IR

  • GOES-R (~2015)

    • No sounder planned


Operational imagers
Operational Imagers Program (HFIP)

  • GOES

    • 5 channel vis/IR

  • Meteosat 2nd generation (east Atlantic)

    • 12 channel SEVERI (vis/IR), east Atlantic

  • POES

    • 6 channel vis/IR AVHRR

  • DMSP

    • vis, IR, Microwave imagers (SSM/I)

  • NPOESS (~2015)

    • Advanced vis/IR imager (VIIRS)

    • Microwave imagery

  • GOES-R (~2015)

    • 16 channel advanced baseline imager (ABI) vis/IR



Assimilation of imager sounder data
Assimilation of Imager/Sounder Data Program (HFIP)

  • Geo – good temporal, spatial, limited spectral (vertical) resolution

  • Polar – Better spectral (vertical) resolution, limited spatial, temporal resolution

  • Coordination of HFIP and JCSDA

    • Community radiative transfer models

    • Surface emissivity algorithms

    • Advanced assimilation techniques

  • Special emphasis for HFIP

    • Assimilation of “cloudy” radiances

    • Techniques to utilize high temporal resolution

    • Assimilation over land

    • Assimilation of lightning data


Jcsda science priorities
JCSDA Science Priorities Program (HFIP)

I Improve Radiative Transfer Models

II Prepare for Advanced Operational Instruments

III Assimilating Observations of Clouds and Precipitation

IV Assimilation of Land Surface Observations from Satellites

V Assimilation of Satellite Oceanic Observations

VI Assimilation for air quality forecasts


New considerations in cloudy radiance assimilation at jcsda
New Considerations in Cloudy Radiance Assimilation at JCSDA

  • Develop forward radiative transfer and Jacobian models including clouds and precipitation

  • Use 1dvar quality control of satellite radiances

  • Extent the control variables with more hydrometeor parameters

  • Incorporate cloud and moisture physics in minimization processes

  • Improve bias corrections using more predictors (e.g. LWP and RWP) from observations and/or moisture physics


NESDIS Ocean Surface Vector Wind (OSVW)

Activities Relevant to HFIP

• Working to establish satellite OSVW as an operationally sustained observing capability

- QuikSCAT follow-on mission

• Striving to fully address NWS OSW requirements needs including tropical cyclones

• NESDIS Ocean Winds flight experiment program

- C- and Ku-band profiling radar system on the NOAA P-3

• Product validation and new product development

• test and evaluate new remote-sensing techniques/sensors

- Real-time data processing, distribution and display system

- Close collaboration with NWS, OAR and AOC during hurricane season experiment

• Research to Operations training and education

- Tailored seminars and training sessions for operational forecasters

- Funding a person at IPC and OPC to help facilitate information exchange between

operational forecasters and remote-sensing experts

• Started with a NOPP funded project (2002), and continued with R20 funding


Surface wind information content from satellite data
Surface Wind Information Content from Satellite Data

  • Surface Wind Information

    • QuikSCAT, ASCAT

    • GOES IR derived proxy flight level winds

      • Relates inner core IR structure to surface winds

    • GOES feature track winds

    • Nonlinear balance winds from AMSU T/q retrievals

  • Simple surface reductions over water/land

  • Combine input in a variational objective analysis system

  • Demonstrates wind information in satellite data

  • New NESDIS operational product in 2009

    • Global tropical cyclone satellite surface wind analysis


Example hurricane boris
Example: Hurricane Boris

GOES-IR Flight-Level Proxy

AMSU 2-d 700 hPa Winds


Example hurricane boris1
Example: Hurricane Boris

GOES winds (P > 600hPa)

Scatterometer


Example hurricane boris2
Example: Hurricane Boris

Surface Analysis

IR Image



Analysis with aircraft obs
Analysis With Aircraft Obs .

Satellite-Only

Satellite+Flight-Level +SFMR

Satellite-only product useful for model verification for cases with no aircraft


Synthetic aperture radar sar
Synthetic Aperture Radar (SAR)

  • Based on cm radar backscatter functional dependence on ocean roughness

  • Early SAR missions had very narrow swath (~100 km)

  • Wider swath (~500 km) available since 1995

    • Canadian Radarsat and European Envisat

  • Sub-km spatial resolution

  • Inner core surface structure

  • Wave information

  • Hurricane applications being developed by NESDIS/StAR (Bill Pichel) + Others


Danielle

31 Aug ‘98

Dennis

27 Aug ‘99

Dennis

29 Aug ‘99

Dennis

31 Aug ‘99

Floyd

15 Sep ‘99

Alberto

17 Aug ‘00

Florence

13 Sep ‘00

Dalila

26 Jul ‘01

Flossie

29 Aug ‘01

Flossie

1 Sep ‘01

Erin

11 Sep ‘01

Erin

13 Sep ‘01

Felix

17 Sep ‘01

Humberto

26 Sep ‘01

Juliette

27 Sep ‘01

Olga

28 Nov ‘01

Hurricane Eye Structure

Hurricane

Eye Wall

Studies

100 km

[CSA Hurricane Watch Project]


Ocean Swell Direction and Wavelength

(C) CSA 1998

Monitoring Storm-generated

Swell Waves

Hurricane Bonnie25 AUG 1998 23:18 UTC


Satellite altimetry
Satellite Altimetry

  • Accurate satellite measurements of ocean surface height provide sub-surface structure

  • NHC has utilized Ocean Heat Content (OHC) analyses qualitatively and in SHIPS statistical model since 2003

  • Assimilation techniques being developed for ocean models



Impact of ohc on operational ships forecasts 2004 2007
Impact of OHC on Operational SHIPS Forecasts (2004-2007)

34 kt or greater, Atlantic storms west of 50oW


Altimeter missions
Altimeter Missions

97

98

99

00

01

02

03

04

05

06

07

08

09

10

11

12

13

14

15

2-f MR & 66-deg, 10-day, 315-km

Jason-2/OSTM

TOPEX-Poseidon

JASON

Jason

Jason-3

SWOT

2017-20?

AltiKa on SARAL

98-deg, 35-day, 80-km – failure of on-board recording

ERS-2

Sentinel 3 – 3-sat series

ENVISAT

HY-2 series

Nearing total failure

108-deg, 17-day, 160-km

data access?

GEOSAT Follow-on

In orbit

Approved

Planned/Pending Approval


Model verification in satellite space
Model Verification in Satellite Space

  • Traditional NHC track/intensity verification provides little insight into causes of errors

  • In situ measurements for verification limited

  • Use forward models to create “synthetic” satellite data from model output

  • Compare synthetic and real satellite data

  • Example from WRF model forecast of an “atmospheric river” from Isadora Jankov et al

  • Similar technique under development at CIRA from NCEP/EMC project


An Evaluation of Various WRF-ARW Microphysics

Using Simulated GOES Imagery

for an Atmospheric River Event Affecting the

California Coast

Isidora Jankov, Manajit Sengupta, Louis Grasso, Daniel Coleman, Dusanka Zupanski,

Milija Zupanski, Lindsey Daniel, and Renate Brummer


Atmospheric River Events

  • During the winter season significant precipitation events in California are often caused by land-falling “atmospheric rivers” associated with extra tropical cyclones in the Pacific.

  • Atmospheric rivers are elongated regions of high values of vertically integrated water vapor over the Pacific and Atlantic oceans that extend from the tropics and subtropics into the extratropics and are readily identifiable using SSM/I.

  • Due to the terrain steepness and soil characteristics in the area, a high risk of flooding and landslides is often associated with these events.


  • SIMULATIONS

  • * 12302005 event

  • 4km inner nest

  • 4 different Microphysics:

  • Lin

  • WSM6

  • Thompson

  • Schultz

  • * YSU PBL

  • NAM Initial

  • and LBCs

ATA

CZD

*

*


CZD 24hr precipitation accumulation starting 30 December, 2005 at 12UTC

Observations

Lin

Schultz

WSM6

Schultz

Thompson


Observations 2005 at 12UTC

Lin

WSM6

Thompson

Schultz

Observed and Simulated

Brightness Temperatures

Valid at 12312005 12UTC


Brightness Temperature’s Probability of Occurrence 2005 at 12UTC

Observations

Lin

WSM6

Thompson

Schultz


Dynamical model evaluation in the context of statistical models
Dynamical Model Evaluation in the Context of Statistical Models

  • Statistical models such as SHIPS and LGEM rely on empirical relationships between intensity change and storm environmental variables

    • SST, shear, etc

  • Do dynamical models properly evolve the large scale environment?

  • Do dynamical models show the same statistical relationships with intensity change?

  • Current HWRF pilot study:

    • Assemble large scale predictor and intensity change database for evaluation


Atlantic intensity model performance
Atlantic Intensity Model Performance Models

2008 Atlantic Intensity Errors (Arthur-Omar)

Atlantic Intensity Model with Smallest 48 hr Intensity Error 1988-2008


Evaluation of hwrf forecasts for hurricane omar
Evaluation of HWRF Forecasts Modelsfor Hurricane OMAR


Observed and hwrf forecasts of sst and vertical shear for omar
Observed and HWRF Forecasts of SST and Vertical Shear for Omar

N = 22, 17, 11 at 0, 36 and 72 hr


Hfip applications of nhc s wind probability model
HFIP Applications of NHC’s OmarWind Probability Model

  • Developed by NESDIS/RAMMB and CIRA

  • Replaced “Strike” probability program in 2006

  • Possible mechanism to tie ensemble forecasts to watches/warning

  • Evaluation of HFIP forecast improvement


The monte carlo wind probability model
The Monte Carlo Wind OmarProbability Model

  • Include uncertainty in TC track, intensity and structure forecasts

  • Interaction of track, intensity structure errors, especially near land, made fitting error distributions to analytic distributions inaccurate

  • Monte Carlo approach useful for situations with complicated geometry, but well-defined interaction rules

    • Originally developed for scattering problems

  • Developed by RAMMB, operational in 2006

  • Text and graphical products

  • Versions for Atlantic and east/central/western N. Pacific


The monte carlo wind probability model1
The Monte Carlo Wind OmarProbability Model

  • 1000 track realizations from random sampling NHC track error distributions

    • Serial correlation and bias of errors accounted for

  • Intensity of realizations from random sampling NHC intensity error distributions

    • Serial correlation and bias of errors accounted for

    • Special treatment near land

  • Wind radii of realizations from radii CLIPER model and its radii error distributions

    • Serial correlation included

  • Probability at a point from counting number of realizations passing within the wind radii of interest


MC Probability Example Omar

Hurricane Dean 17 Aug 2007 18 UTC

  • Major Hurricane

  • Non-major Hurricane

  • Tropical Storm

  • Depression

1000 Track Realizations 64 kt 0-120 h Cumulative Probabilities


Objective guidance for u s hurricane warnings
Objective Guidance for OmarU.S. Hurricane Warnings

  • Preliminary rules based on probability analysis for NHC hurricane warnings

    • Use 48 h cumulative 64 kt probabilities

    • Add breakpoint if P ≥ 10 %

    • Remove breakpoint if P < 1%

    • Minimum warning length = 40 n mi

      • Except near U.S. boundaries

    • Update every 6 hours

  • Rules for Watches under development


Hurricane ivan 2004 example

3 Omar

Hurricane Ivan 2004 Example

NHC Objective Guidance

13 Sep 00 Z

15 Sep 06 Z

(24 h before

landfall )


Ivan hurricane warning lengths
Ivan Hurricane Warning Lengths Omar

  • Objective Guidance

    • Warnings issued a little earlier

    • Warning lowered a little earlier

    • Warnings for Dry Tortugas

    • Gulf coastline length about right


Evaluation of impact of model improvements on watches warnings
Evaluation of Impact of Model Improvements on Watches/Warnings

  • Hurricane Ivan case

  • What is impact on warnings from a 20% and 50% reduction in track error?

    • Adjust track errors in MC model by 20% and 50%

    • Run automated warning program using adjusted probabilities

    • Calculate reduction of coastline length for each case


Warning length reductions n mi due to reduced track errors
Warning Length Reductions (n mi) Watches/WarningsDue to Reduced Track Errors

Note: Duration of the warnings also reduced by 6 to 18 hours


Forecast dependent probabilities
Forecast-Dependent Probabilities Watches/Warnings

  • Operational MC model uses basin-wide track error distributions

  • Can situation-dependent track distributions be utilized?

Track plots courtesy of J. Vigh, CSU


72 hr atlantic nhc along track error distributions stratified by gpce 2002 2006
72 hr Atlantic NHC Along Track Error Distributions Stratified by GPCE*(2002-2006)

*GPCE is Goerss Predicted Consensus Error, which depends on track model spread


Mc model with track errors from upper and lower gpce terciles
MC Model with Track Errors from Stratified by GPCE*Upper and Lower GPCE Terciles

Lower Tercile Distributions Upper Tercile Distributions

Hurricane Frances 2004 01 Sept 00 UTC Example

120 hr Cumulative Probabilities for 64 kt


Summary
Summary Stratified by GPCE*

  • NESDIS has contributions to make to HFIP

  • Coordination with JCSDA on radiative transfer modeling and radiance assimilation

  • Utilization of scatterometer, SAR and altimetry data

  • Model verification and improvements through satellite and statistical model comparisons

  • “Societal benefit” analysis and application of ensembles through NHC’s wind probability model


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