The 10th JCSDA Workshop on Satellite Data Assimilation,October 10-12, 2012, NCWCP, College Park, MD

Regional Data Assimilation

Utility of GOES-R instruments for hurricane data assimilation

Milija Zupanski1, Man Zhang1, Karina Apodaca1, Louie Grasso1, John Knaff2

1 Cooperative Institute for Research in the Atmosphere2 NOAA/STAR/RAMMB

Colorado 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

Goals of the project

(1) Examine the utility of the GOES-R ABI and GLM instruments for hurricane inner-core assimilation and prediction

(2) Enhance the system by adding full spatial resolution advanced IR sounding product (in collaboration with Jun Li, CIMSS)

(3) 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)

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

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

Observation Transformation OperatorEnsemble + Control

MINIMIZATION

CONTROL VARIABLE UPDATE

xf ; Pf

xa; Pa

HWRF ModelEnsemble + Control

Pre

dict

ion

step

Ana

lysi

s st

ep

DA cycles

- GSI observations- GOES-R ABI all-sky radiances- GOES-R GLM lightning- AIRS/IASI advanced IR sounding profiles

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

• 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

HWRF model

Forward GSI/CRTM

operator

HWRF-MLEF interface

GSI/CRTM-MLEF interface

MLEF

HWRF ensemble

GSI/CRTM ensemble

Forecast error covariance in hurricane inner core

• 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

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

Forecast error covariance is fundamental for data assimilation Complex, flow-dependent inter-variable correlations

HWRF-MLEF Pf auto-correlationssingle observation of specific humidity at 850 hPa

Well-defined, localized analysis response

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).

X

Vertical cross-section of Q analysis

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

Q analysis at 850 hPa

HWRF-MLEF analysis response

HWRF-MLEF Pf cross-correlationssingle observation of specific humidity at 850 hPa

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

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.

HWRF-MLEF analysis response

PD (=ps-pt) analysis Vertical cross-section of U-wind analysis

X

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

Satellite observation information using Shannon information measures

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

In ensemble DA methods DFS can be computed exactly in ensemble subspace:

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

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

Change of entropy due to observations

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

Entropy

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 mm:HWRF inner domain

- Hurricane Fred (2010)- 10 km data thinning

SEVIRI Tb before and after thinning CWM analysis increment Information content - DFS

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

- 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

- Hurricane Fred (2010)- No data thinning for SFOV profiles

Information content - DFS

MSG SEVIRI only

MSG SEVIRI and AIRS q

profile

MSG SEVIRI and AIRS T

profile

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

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 forecastUse 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

Information content

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

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

Forecast RMS error

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

Summary

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

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.

Additional details and publications from this project

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.