Milija Zupanski Cooperative Institute for Research in the Atmosphere Colorado State University

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JCSDA Science Workshop on Satellite Data AssimilationJune 5-7, 2013, NCWCP, College Park, MD

Utility of GOES-R ABI and GLM instruments in

regional data assimilation for high-impact weather

Milija ZupanskiCooperative Institute for Research in the

AtmosphereColorado State University

Programs:

• GOES-R Risk Reduction• JCSDA• NSF Collaboration in Mathematical Geosciences (NSF-CMG)

People:- Karina Apodaca (CIRA), Man Zhang (CIRA), Lous Grasso (CIRA)- Mark DeMaria (NOAA/STAR), John Knaff (NOAA/STAR) - Min-Jeong Kim (CIRA-NOAA/EMC) - Jun Li (CIMSS, Univ. Wisconsin)

High-End Computing:- NOAA S4 (SSEC, Univ. Wisconsin)- NCAR CISL (Yellowstone)

Acknowledgements

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Objectives:- Assess the benefit of future GOES-R observations (ABI and GLM)- Use realistic NOAA systems - Conduct necessary development to maximize the benefit of GOES-R

The research encompasses:- assimilation of cloudy radiances (infrared and microwave)- cloud-resolving data assimilation applications to high-impact weather - regional hybrid variational-ensemble data assimilation

The following components are incorporated: Weather Research and Forecasting Model (WRF):

- WRF-NMM, WRF-ARW, hurricane WRF (HWRF)

Observation operator:- NCEP Gridpoint Statistical Interpolation (GSI)- Community Radiative Transfer Model (CRTM)

Data assimilation:- Maximum Likelihood Ensemble Filter (MLEF) - Use of regional hybrid GSI in the future

Research

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• Severe weather Thunderstorms Tornadoes Rainfall Hail Flash floods

High-impact weather and clouds

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GOES-R

Wide-spread supercell thunderstorms – indicated by clouds

Moore tornado (05/20/2013)

High-impact weather and clouds

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• Tropical cyclones High winds Storm surge Rainfall Floods Tornadoes Rip currents

Improving assimilation and prediction of clouds is critical for reducing uncertainty of high-impact

weather

Hurricane Sandy (2012)

• Nonlinearity (and non-differentiability) Microphysical processes Radiative Transfer (RT) model

• Forecast error covariance Flow-dependent, cross-variable correlations, microphysics, dynamics

• Computational limitations High-dimensional state vector for cloud-resolving data assimilation Required additional RT model calculations

Assimilation of clouds for high-impact weather

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GOES-R • Clouds are observed by satellites Microwave (MW), Infrared (IR) Lightning (future GLM): Signature of high-impact weather and clouds

Cloudy satellite radiances are generally not assimilated in weather operations:

Potentially useful information about clouds is mostly discarded. Can we do better?

Research scope and tools

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MW: precipitation-affected radiances (microphysics/precipitation) IR: cloud-affected radiances (cloud top/location) Lightning flash rate (cloud ice, storm environment) AIRS SFOV vertical profiles of q and T (storm environment)

Assimilate observations that can add new information about clouds and the storm environment

hurricane WRF (HWRF) WRF-NMM

Regional data assimilation of tropical cyclone core area and severe weather

Data assimilation components Maximum Likelihood Ensemble Filter (MLEF) Forward GSI Forward CRTM

A hybrid between EnKF and variational methods- iterative minimization (variational)- multiple realizations of model and observation operators for uncertainty

(ensemble) Deterministic control forecast Nonlinear analysis solution by an iterative minimization Improved minimization efficiency by an implicit Hessian preconditioning

Data assimilation algorithm: Maximum Likelihood Ensemble Filter (MLEF)

8Fast convergence (1-2 iterations) from an arbitrary initial state

MLEF is modular

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Observation module: - Transform/interpolate from

model to observations- Read observations- Output observation info:

increments, errors, …

Forecast module: - Import the forecasting system

with pre- and post-processing- Make namelist.input on-the-

fly

Data assimilation module: - Controls the processing of the

model and observation info- Hessian preconditioning,

gradient, minimization iterations- State vector and uncertainty

estimates

Since the singular values s are by-product of assimilation, the flow-dependent DH and DFS can be computed

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

Gaussian pdf greatly reduces the complexity since entropy is related to covariance

Change of entropy due to observationsEntropy

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Assessment: Quantifying satellite information using Shannon information measures

Change of entropy Degrees of freedom for signal (DFS)

All-sky microwave radiance assimilation in Tropical Cyclone core area

• Model: NOAA HWRF (operational in 2011, 27km/9km)

• Results for TC core area (inner nest) at 9 km resolution

• Observations: AMSU-A all-sky radiances, Channels 1-9 and 15 assimilated

• Data assimilation interval: 6 hours

• Number of ensembles: 32

• Hurricane Daniele (2010)

• Control variable: pd, T, q, u, v, cwm

• Bias correction from clear-sky GSI output

• From M. Zhang et al. (2013a, Mon. Wea. Rev.) 11

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Hurricane Danielle (2010): All-sky AMSU-A information content (DFS)

Cycle 1 Cycle 3 Cycle 5 Cycle 7

ASR

CSR

Cloudy radiance observations add new information throughout the storm development

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(a) IR imagery(b) AMSU-A retrieved

precipitation rate

(c) CTL – control experiment(d) ASR – all-sky experiment

Hurricane Danielle (2010)6-hour forecast of total cloud condensate

Data assimilation cycle 8 – valid 1200 UTC 26 Aug 2010

(a)

(c)

(b)

(d)

All-sky MW radiances improve short-range forecast of clouds in hurricane core area

Hurricane Danielle (2010): Time series of the minimum sea level pressure (hPa) for NHC best track data (thick grey line) and MLEF-HWRF experiments ASR (solid) and CSR (dashed) between 1800 UTC 24 Aug and 1800 UTC 26 Aug 2010.

Hurricane Danielle (2010): Intensity

All-sky infrared radiance assimilation for Tropical Cyclone core area

• Model: NOAA HWRF (operational in 2011, 27km/9km)

• Results for TC core area (inner nest) at 9 km resolution

• Observations: SEVIRI all-sky radiances [10.8 mm - proxy for GOES-R ABI)

• Data assimilation interval: 1 hour

• Number of ensembles: 32

• Hurricane Fred (2009)

• No bias correction (advantage of clear-sky GSI correction not obvious)

• From M. Zhang et al. (2013b) 15

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Hurricane Fred (2009): Analysis

All-sky radiance assimilationControl experiment Verification: AMSU-A NOAA-16 retrieved cloud liquid water

Assimilation of all-sky infrared radiance is able to improve clouds in TC core

Total cloud condensate (cwm)Valid 0600 UTC 9 Sep 2009

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Hurricane Fred (2009): 21-hour forecastTotal cloud condensate (cwm)

Valid 0300 UTC 10 Sep 2009

All-sky radiance assimilationControl experiment Verification: Seviri radiance observations

Assimilation of all-sky infrared radiance improves the forecast of clouds in TC core area

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

Degrees of freedom for signal - DFS

MSG SEVIRI only

MSG SEVIRI and AIRS q

profile

MSG SEVIRI and AIRS T

profile

In outer domain (with less clouds) DFS shows a benefit from AIRS SFOV specific humidity and temperature data (temperature has a stronger impact)

Hurricane Fred (2009): Assimilation of all-sky SEVIRI and AIRS SFOV (q,T) in HWRF outer domain

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Lightning data assimilation: Severe weather applications

• Model: NOAA WRF-NMM (27km/9km)

• Results for the inner nest at 9 km resolution

• Observations: WWLLN [proxy for GOES-R GLM)

• Data assimilation interval: 6 hours

• Number of ensembles: 32

• Tornado outbreak over Southeast US in April 2011

• From Apodaca et al. (2013)

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Model domain and tornado reports for April 27, 2011

Severe weather outbreak over the southeastern US on April 25-18, 2011

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D01

D02

Lightning observation operator

• WWLLN can observe only cloud-to-ground (C-G) flashes

• GLM will observe both C-G and intra-cloud flashes

• Regression between lightning flash rate and model variables- Best regression suggests cloud ice and vertical graupel flux (McCaul et al. 2009)

• WRF-NMM microphysics (Ferrier) does not predict cloud ice: - Need to rely on less accurate regression: maximum vertical

updraft

• Present: - Use max vertical updraft and WWLLN

• Future: - Include more complex microphysics to improve obs operator- Use better GLM proxy observations (C-G and intra-cloud)- Increase the resolution to 1-3 km 21

2011_04_27-00:00:00Cycle 1

2011_04_27-12:00:00Cycle 3

2011_04_28-00:00:00Cycle 5

WWLLN ObservationsWWLLN Observations WWLLN Observations

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Information content of lightning observations: DFS

Flow dependent information added by assimilating lightning data

Lightning data assimilation with MLEF: Single observation experiment

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Wind increment Temp increment Psfc increment

Assimilation of lightning observations impacts all model variables and improves storm environment conditions: important for maintaining impact of assimilation

Analysis response to a single observation of flash rate in a 6-hour interval

Possible new research directions

(1) Combine all observations in applications to TC/severe weather

- All-sky infrared radiances(GOES-R ABI)- Lightning (GOES-R GLM)- All-sky microwave radiances- AIRS/IASI (sounder)- NOAA operational observations (GSI)

(2) Extend the utility of GOES-R instruments to air quality

- Impact of lightning on NOx and O3 (air quality)- Use ABI and GLM for high-impact weather and AQ

(3) Examine the impact of WV channels for TC genesis- channel difference contains important information

(J. Knaff)

(4) Further development of hybrid variational-ensemble systems

- hybrid forecast error covariance with optimal Hessian preconditioning 24

Hybrid data assimilation• Extend MLEF to include static/variational and ensemble error

covariances- Hybrid ensemble-variational error covariance with optimal Hessian

preconditioning- Single DA system (no separate variational and ensemble algorithms)

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ensstatic

• Potential improvements over the existing hybrid DA systems:- Optimal Hessian preconditioning (e.g., observation term included)- Considerable computational savings: adaptive ensemble

forecasting- Single DA system is easier to maintain than two systems (Var and

EnKF)

Potential relevance to operations

(1) Utilize the development of the MLEF regional hybrid data assimilation for all-sky radiances and GLM to improve hybrid GSI

- observation processing and QC is same (GSI)- evaluated in realistic situations using NOAA systems- reduced code transfer/development- explore the utility of MLEF ensembles in regional hybrid

GSI

(2) Add GLM assimilation capability to operational DA system

- New observations accessed through GSI and CRTM- BUFR format

(3) Regional assimilation of combined GLM and all-sky ABI observations for warn-on-forecast and hurricane prediction

- Results show relevance of these observations for prediction of clouds

- Could be used in combination with hybrid GSI or as a standalone system for high-resolution nest (e.g., TC core, WRF-NMM inner nest)

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Future work

• Combine all observations in applications to TC/severe weather

- All-sky infrared radiances(GOES-R ABI)- Lightning (GOES-R GLM)- All-sky microwave radiances- AIRS/IASI (sounder)- NOAA operational observations (GSI)

• Examine the impact of WV channels for TC genesis

• Impact of lightning on NOx and O3 (air quality)- Use ABI and GLM for high-impact weather and AQ

• Coupled models (atmosphere-chemistry-land-ocean)

• Further development of hybrid variational-ensemble systems

- hybrid forecast error covariance with optimal Hessian preconditioning

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Thank you!References:Apodaca, K., M. Zupanski, M. DeMaria, J. Knaff, and L. Grasso, 2013: Evaluating the potential impact of assimilating satellite lightning data utilizing hybrid (variational-ensemble) methods. Submitted to Tellus. Zhang, M., M. Zupanski, M.-J. Kim, and J. Knaff, 2013: Assimilating AMSU-A Radiances in TC Core Area with NOAA Operational HWRF (2011) and a Hybrid Data Assimilation System: Danielle (2010). Mon. Wea. Rev., in print.Zupanski M., 2013: All-sky satellite radiance data assimilation: Methodology and Challenges. Data Assimilation for Atmospheric, Oceanic, and Hydrologic Applications (Vol. II), S.-K. Park and L. Xu, Eds, Springer-Verlag Berlin Heidelberg, 730 pp.

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