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Acknowledgements : Jun Li (CIMMS/SSEC, UW-Madison) JCSDA NOAA NESDIS/GOES-R PowerPoint Presentation
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slide1

The 10th JCSDA Workshop on Satellite Data Assimilation,October 10-12, 2012, NCWCP, College Park, MD Regional Data AssimilationUtility of GOES-R instruments for hurricane data assimilationMilija Zupanski1, Man Zhang1, Karina Apodaca1, Louie Grasso1, John Knaff21 Cooperative Institute for Research in the Atmosphere2NOAA/STAR/RAMMBColorado State UniversityFort Collins, Colorado, U. S. A.[ http://www.cira.colostate.edu/projects/ensemble/ ]

  • Acknowledgements:
  • Jun Li (CIMMS/SSEC, UW-Madison)
  • JCSDA
  • NOAA NESDIS/GOES-R
slide2

Goals of the project

  • Examine the utility of the GOES-R ABI and GLM instruments for hurricane inner-core assimilation and prediction
  • Enhance the system by adding full spatial resolution advanced IR sounding product (in collaboration with Jun Li, CIMSS)
  • Use NOAA operational codes and scripts:
  • Benchmark system incorporates NCEP operational observations and modeling systems
  • - NOAA HWRF (NMM)
  • - GSI, CRTM
  • - Hybrid variational-ensemble DA system (MLEF-Maximum Likelihood Ensemble Filter)
  • Enhanced system can include additional assimilation of:
  • - All-sky SEVIRI IR radiances (proxy for ABI)
  • - WWLLN lightning flash rate (proxy for GLM)
  • - Vertical profiles of T and Q (AIRS, IASI)
  • - Assimilation of all-sky MW radiances (AMSU-A)
slide3

Time schedule

  • Tasks:
  • Year 1 (2011): Initial development and evaluation of the regional HWRF DA system
  • Interface the MLEF-HWRF system with MSG SEVIRI observations as proxy for GOES-R ABI.
  • Make initial evaluation of the system by comparing assimilation/forecast results with and without MSG SEVIRI data in application to tropical cyclones (TC).
  • Develop the full spatial resolution advanced sounding product in clear and some cloudy skies (e.g., thin cloud and low clod situations) and error characterization.
  • Add capability to assimilate all-sky MW radiances (AMSU-A)
  • Year 2 (2012): Combine all components, with exception of lightning data
  • Combine all components into a single ensemble-based data assimilation system and conduct benchmark experiments (without lightning data).
  • Evaluate the impact of GOES-R ABI proxy data in TC application.
  • Evaluate the impact of IR sounder proxy data in clear skies in TC application.
  • Year 3 (2013): Lightning data and final evaluation
  • Include lightning data in the MLEF-HWRF system.
  • Include advanced IR sounding product in cloudy region, evaluate the impact of full spatial resolution cloudy soundings.
  • Conduct a thorough evaluation of the value-added impact of lightning data in TC data assimilation applications.
mlef hwrf flow diagram

HWRF Model

Ensemble + Control

Prediction step

xf ; Pf

Observation Transformation Operator

Ensemble + Control

MINIMIZATION

Analysis step

CONTROL VARIABLE UPDATE

xa; Pa

DA cycles

MLEF-HWRF FLOW DIAGRAM

- GSI observations

- GOES-R ABI all-sky radiances

- GOES-R GLM lightning

- AIRS/IASI advanced IR sounding profiles

MLEF:

1- iterative minimization of a cost function is applied to obtain a nonlinear solution to data assimilation problem

2- flow-dependent error covariance estimation

Iterations

slide5

MLEF-HWRF code organization

Modular structure:

- HWRF, GSI, and MLEF are independent modular components

- it allows straightforward updating of each component without affecting other system components

HWRF-MLEF interface

HWRF model

HWRF ensemble

MLEF

Forward GSI/CRTM operator

GSI/CRTM-MLEFinterface

GSI/CRTM ensemble

  • New observations included through GSI/CRTM
  • - BUFR read, interpolate, transform, quality control
  • Required MLEF module in GSI (e.g., to create forward)
  • HWRF used without any change
forecast error covariance in hurricane inner core
Forecast error covariance in hurricane inner core
  • Forecast error covariance is fundamental for data assimilation
  • Complex, flow-dependent inter-variable correlations

Correlations between DYNAMICAL variables

Correlations between MICROPHYSICAL variables

Cross-correlations between DYNAMICAL (pd, t, q, u, v) and MICROPHYSICAL (e.g., cwm, qice, qcld, qgrp, qrain, qsnow) variables

  • Unknown correlations among microphysical variables and between dynamical and microphysical variables
  • Principal advantage for hybrid and ensemble-based data assimilation since previous knowledge of correlations is not required, being produced by HWRF ensemble forecasts
  • Fundamentally important to have a “good” estimate of forecast error covariance since in DA all observation increment are projected onto that subspace
hwrf mlef p f auto correlations single observation of specific humidity at 850 hpa
HWRF-MLEF Pf auto-correlationssingle observation of specific humidity at 850 hPa

Hurricane Gustav (2008) – HWRF moving nest at 9 km resolution

HWRF-MLEF analysis response

Vertical cross-section of Q analysis

Q analysis at 850 hPa

X

The results are valid for hurricane Gustav (2008) at 1200 UTC on August 31, 2008. The cross denotes the location of observation and vertical response is plotted along the dashed line (26.75 N).

Well-defined, localized analysis response

hwrf mlef p f cross correlations single observation of specific humidity at 850 hpa
HWRF-MLEF Pfcross-correlationssingle observation of specific humidity at 850 hPa

Hurricane Gustav (2008) – HWRF moving nest at 9 km resolution

HWRF-MLEF analysis response

Vertical cross-section of U-wind analysis

PD (=ps-pt) analysis

X

The results are valid for hurricane Gustav (2008) at 1200 UTC on August 31, 2008. The cross denotes the location of observation and vertical response is plotted along the dashed line.

An increase of specific humidity implies a reduction of surface pressure and a stronger convergence/vortex near the center of hurricane

satellite observation information using shannon information measures
Satellite observation information using Shannon information measures

Use Shannon information theory (e.g. entropy) as an objective, pdf-based quantification of information (Rodgers 2000; Zupanski et al. 2007)

Entropy

Change of entropy due to observations

  • Gaussian pdf greatly reduce the complexity since entropy is related to covariance

Change of entropy / degrees of freedom for signal (DFS)

In ensemble DA methods DFS can be computed exactlyin ensemble subspace:

Since eigenvalues of the matrix ZTZ are known and the matrix inversion is defined in ensemble space, the flow-dependent DFS can be computed

experimental setup
Experimental setup
  • Data assimilation setup:
  • NOAA Hurricane WRF(NMM) model at 27km / 9km resolution
  • Moving nest
  • Use MLEF as a prototype HVEDAS
  • 32 ensembles
  • 6-hour assimilation interval
  • Control variables: PD, T, Q, U, V, CWM
  • Benchmark system observations:
  • NOAA operational observations using GSI and CRTM forward operators
  • Enhanced system observations:
  • All-sky AMSU-A radiances: use the approach originally developed by M.-J. Kim for global DA system, adapted by M. Zhang for use in regional DA system and for hurricanes
  • All-sky MSG SEVIRI IR radiances (proxy for GOES-R ABI)
  • AIRS, IASI vertical profiles of temperature and specific humidity
  • Focus on the hurricane inner core (moving nest at 9 km resolution)

Additional details presented on the poster by Man Zhang et al.

assimilation of all sky msg seviri ch9 10 8 m m hwrf inner domain
Assimilation of all-sky MSG SEVIRI Ch9 – 10.8 mm:HWRF inner domain
  • Hurricane Fred (2010)
  • 10 km data thinning

SEVIRI Tb

before and after thinning

Information content - DFS

CWM analysis increment

  • Loss of data due to thinning used in GSI
  • - need to enhance data usage and improve quality control
  • Positive impact on the analysis in the center of TC
assimilation of all sky msg seviri and airs sfov q t hwrf inner domain
Assimilation of all-sky MSG SEVIRI and AIRS SFOV (q, T):HWRF inner domain
  • Hurricane Fred (2010)
  • No data thinning for SFOV profiles

CWM analysis increment

Information content - DFS

MSG SEVIRI and AIRS SFOV q profile only

MSG SEVIRI and AIRS SFOV T profile only

  • Analysis increment for clouds (cwm) benefits more from q data than from T data
  • Need to examine relative impact for cloudy profiles of q and T
assimilation of all sky msg seviri and airs sfov q t hwrf outer domain
Assimilation of all-sky MSG SEVIRI and AIRS SFOV (q, T): HWRF outer domain
  • Hurricane Fred (2010)
  • No data thinning for SFOV profiles

Information content - DFS

MSG SEVIRI and AIRS q profile

MSG SEVIRI and AIRS T profile

MSG SEVIRI only

In outer domain (with less clouds) DFS shows more benefit from AIRS SFOV T data than from q data

assimilation of wwlln lightning flash rates goes r glm proxy preparation for next year
Assimilation of WWLLN lightning flash rates (GOES-R GLM proxy): Preparation for next year
  • Two main approaches for observation operator:
  • Use maximum vertical velocity as an input to regression equation
  • - sensitivity of dynamical control variables (pd, T, q, u, v) important for dynamically balanced impact on the analysis and forecast
  • Use cloud hydrometeor-based lightning observation operator (McCaul et al. 2009)
  • - relevance of graupel flux and vertically integrated cloud ice for lightning assimilation.
  • - still possible to maintain sensitivity to dynamical control variables

Preliminary results: Assimilation of WWLLN lightning observations using WRF-NMM

Information content

Forecast RMS error

  • Information content shows the utility of lightning observations in the analysis
  • 6-hour WRF-NMM forecast improved due to lightning observations
slide15

Summary

  • HWRF, GSI/CRTM and MLEF combined in a prototype regional HVEDAS
  • Assimilation of all-sky MSG SEVIRI IR radiances
  • Assimilation of advanced IR sounding products
  • Preliminary results encouraging
  • Need improvements in data selection, quality control
  • Preliminary development of lightning data assimilation encouraging in WRF-NMM

Future Work

  • Include lightning data in the MLEF-HWRF system.
  • Include assimilation of advanced IR sounding product in cloudy regions
  • Conduct a thorough evaluation of the value-added impact of lightning data in regional hurricane data assimilation applications
  • Demonstrate the utility of GOES-R ABI and GLM, and advanced IR soundings in regional hurricane DA
additional details and publications from this project
Additional details and publications from this project

Apodaca, K., M. Zupanski, M. Zhang, M. DeMaria, L. D. Grasso, J. A. Knaff, and G. DeMaria:Evaluating the potential impact of assimilating GOES-R GLM satellite lightning observations. To be submitted to Mon. Wea. Rev.

Zhang, M., M. Zupanski, M.-J. Kim, and J. Knaff, 2012: Direct Assimilation of all-sky AMSU-A Radiances in TC inner core: Hurricane Danielle (2010). Mon. Wea. Rev., in review.

Zupanski M., 2012: All-sky satellite radiance data assimilation: Methodology and Challenges. Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications, S.-K. Park and L. Xu, Eds, Springer-Verlag Berlin, in print.

Also, see the poster “Investigating the effects of GOES-R measurements and advanced infrared soundings for hurricane core region data assimilation using a hybrid data assimilation system” by Man Zhang et al.