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Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation Ralf Bennartz 1 , Tom Greenwald 2 , Andrew Heidinger 3 , Chris O’Dell 1 , Martin Stengel 1 , Kenneth Campana 4 , Peter Bauer 5 1: Atmos. & Oceanic Sci.,University of Wisconsin 2: CIMSS,University of Wisconsin 3: NOAA/NESDIS 4: NOAA/NCEP 5: ECMWF
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Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Dec 31, 2015

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Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation Ralf Bennartz 1 , Tom Greenwald 2 , Andrew Heidinger 3 , Chris O’Dell 1 , Martin Stengel 1 , Kenneth Campana 4 , Peter Bauer 5 1: Atmos. & Oceanic Sci. ,University of Wisconsin 2: CIMSS ,University of Wisconsin - PowerPoint PPT Presentation
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Page 1: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Ralf Bennartz1, Tom Greenwald2, Andrew Heidinger3,

Chris O’Dell1, Martin Stengel1, Kenneth Campana4, Peter Bauer5

1: Atmos. & Oceanic Sci.,University of Wisconsin2: CIMSS,University of Wisconsin3: NOAA/NESDIS4: NOAA/NCEP5: ECMWF

Page 2: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Outline

SOI model and applications

Cloud/precipitation overlap

3-year accomplishments

Further plans

Page 3: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

SOI Radiative Transfer Model

• Hybrid multi-stream solution method (doubling plus iteration)

(Heidinger et al. 2006; O’Dell et al. 2006)

• Implementation

- Less scattering: 2-stream solution

- More scattering: 4-stream solution

• Forward, tangent linear and adjoint models integrated into CRTM

Page 4: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Accuracy of Results (Eddington and SOI versus Monte-

Carlo model)

Page 5: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Forward and adjoint simulation example

AMSR-E ObservationsGFS Simulations

Page 6: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Infrared Applications: MSG SEVIRI

Page 7: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

MSG SEVIRI Comparison Results

Channel RMSE [K] Bias [K]

6.2 m 1.93 0.22

7.3 m 1.91 -1.25

8.7 m 1.54 1.17

10.8 m 1.38 0.73

12.0 m 1.37 0.64

13.4 m 1.37 1.06

Page 8: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Different cloud/precipitation overlap models

• Conventional approach uses cloud cover to subdivide NWP pixel in cloudy/precipitation

• New approach derives two or three optimal columns based on subscale distribution of precipitation columns with similar optical properties

• Numerically efficient (2-3 radiative transfer calculations per NWP grid point)

• Highly accurate against independent column/Max-Random-overlap reference

• Optimal approach reduces errors due to cloud overlap from maximum values of 5-10 K to values < 1K

Page 9: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Different cloud/precipitation overlap models

O’Dell, Bauer, Bennartz, JAS, 2006, submitted

Currently operational at

ECMWF

1Column 2Columns 3columns 3 optimal

Page 10: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Different cloud/precipitation overlap models

Currently operational at

ECMWF

New scheme with much better error

characteristics

O’Dell, Bauer, Bennartz, JAS, 2006, submitted

1Column 2Columns 3columns 3 optimal

Page 11: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

3-year Accomplishments

• Fast forward RT model (SOI) developed, tested and integrated in CRTM (3 publications)

• Tangent linear and adjoint model developed, tested, and integrated in CRTM

• Bias GFS/SOI statistics for passive microwave and infrared

• Fast and accurate new cloud overlap scheme for use in NWP radiance assimilation (forward/adjoint); manuscript submitted

Page 12: Towards Passive Microwave Radiance Assimilation of Clouds and Precipitation

Further plans

• Monitor bias statistics over longer time period, especially:

– Fully include scattering (need more complete GFS input data)

– Biases in IR including scattering • Precipitation assimilation:

– Include cloud diagnostics to generate precipitation rate

– Further test and integrate cloud overlap with NCEP/GFS.