1. Steve Albers, Kirk Holub, Yuanfu Xie (CIRA & NOAA/ESRL/GSD)
GPM: Radar Observations and Simulations with the Local Analysis and Prediction System (LAPS)
Steve Albers, Kirk Holub, Yuanfu Xie (CIRA & NOAA/ESRL/GSD)
Updated 9/21/ UTC

2. Introduction to LAPS
System used for data assimilation along with forecast model initialization / post-processing
High resolution and Rapid Update
Assimilates a wide variety of in-situ and remotely sensed data
Portable efficient software implemented at various institutions over several decades
Federal, state government agencies
Private sector, academia, international
Used to analyze and forecast clouds + related sky conditions
Analyses (with active clouds) are used to initialize a meso-scale forecast model (e.g. WRF)

3. LAPS Cloud analysis 11um,VIS,3.9um First Guess + Observations METAR
(Albers et. al. 1996) 3

4. TRMM / GPM

5. First sequence of steps in cloud analysis

6. Second sequence of steps in cloud analysis

7. Status of Simulations
TRMM case has been selected (July 26, UTC)
Convection occurring over Florida
Radar data remapped to Cartesian model grid as “look-alike” ground based reflectivity
Tampa Bay and Melbourne simulated radars
Evaluation of analyses and forecasts is ongoing
Three assimilation experiments using non-radar obs plus
TRMM radar
Ground-based radar
Neither source of radar

8. Radar Reflectivity Space Based
Model Initialization
15 minute forecast

9. Radar Reflectivity Initial Condition to 15 minute forecast loop
Ground Based Radar
Space Based Radar

10. Radar Reflectivity Initial Condition to 15 minute forecast loop
No Radar
Space Based Radar

11. Big Picture Considerations for Precip
GPM Core Observatory radar coverage is limited in space and time (when used alone)
Potentially less operational weather model benefit
4DVAR can help increase impact (particularly in a global model)
Spreads obs in time and space
Helps compensate for latency
Use GPM radar / MI data to calibrate microwave data from other GPM constellation satellites
More frequent satellite microwave passes compared with radar
Hydrometeor climatological covariance between various species
Fill in ice phase information
Leverage climate related research for use in weather models

12. Variational Cloud Analysis
Approach
Hydrometeor control variables
Variational – solves entire problem at once
Based on “traditional” LAPS cloud analysis
Modular – can be used in GSI, LAPS, etc
Observations
Satellite (e.g. GPM, GOES)
Insitu (e.g. LIDAR, cameras)
Use radiance or retrievals with each platform
Thermodynamical & microphysical constraints
Status
Under Development

13. Variational Cloud Analysis Strategies
Choose Object / Fwd Model for Satellite / Radar Obs
Constraints
Physical
Statistical OR OR OR
Water Vapor RH & Q
Saturation Q
These five constants represents the coefficient of hydrometer concentration, with the unit of 1/kg/m.
Cloud optical depth is equal to the integration of hydrometers from surface to model top vertically.
Cloud Tau
or LWP
Retrievals
IR B-temp
VIS Albedo
Radar Refl.
Goddard Satellite
Simulator
CRTM

14. Thanks!

15. Variational Cloud Analysis
Modular variational cloud analysis currently under development
Based on existing LAPS and GSI cloud analyses
Simultaneous solution allowing merging of GPM and other satellite measurements
Hydrometeor control variables (qc, qi, qr, qs, qg)
Use satellite radiance (e.g. CRTM) or retrievals (e.g. DCOMP)
Appropriate forward models and physically based constraints
Include all-sky cameras as input data
Presently under development at GSD
Assess physical and statistical constraints
Compare various approaches for GSI inclusion

16. Variational Cloud Observation Operator
Courtesy Juxiang Peng
Cloud optical depth (satellite retrieval) =
hydrometeor water content analyzed as control variables
Units: m
Hydrometeor Water Path
These five constants represents the coefficient of hydrometer concentration, with the unit of 1/kg/m.
Cloud optical depth is equal to the integration of hydrometers from surface to model top vertically.
constants account for
scattering cross-sections
effective particle radius

17. Variational impact on Cloud Ice Content at 225mb Sept. 13, 2013 1500UTC
Here are the cloud ice at 225hPa.The left is analyze from using cloud optical depth ,and the right is analyze from using traditional LAPS
The left Cloud ice seems add some detail characteristic when compare with LAPS
vLAPS + Satellite Cloud Optical Depth
vLAPS + non-var Cloud

18. Observation Operator with Temperature Constraints Added on Hydrometer Type
Cloud phase
In order to use cloud phase as constrain condition in minimization, add new coefficient in front of each hydrometer. The new coefficients control the hydrometer as switch. It’s value is based on Background temperature
Here the cloud ice and cloud water reference temperature temporarily set to be zero.
Courtesy Juxiang Peng

19. Cloud Ice Content along 40N Sept 13, 2013 1500 UTC
The Y axis is pressure of STMAS,and X axis is longitude .The left is from COD scheme and the right is from LAPS scheme.
Here is obviously diffenence between the two scheme.On the left hand ,the hight of cloud ice is higher.
Tradition LAPS analysis cloud height from 1200 m to 20000m. STMAS analysis cloud height from sfc to model top.
The large height of cloud ice corresponding to convective area.
kg/meter**3
kg/meter**3
vLAPS + Satellite Cloud Optical Depth
vLAPS + non-var Cloud
Courtesy Juxiang Peng

20. Variational Cloud Analysis Experiments
CRTM
Forward model
K-matrix model
Minimization
Surface Info
Control variables
Simulated radiances
Gradients
Atmospheric state along trace
Surface characteristics in FOV
Sensor and source locations
Special setup for each obs profile
Load coefficients
Atmosphere structure
Surface structure
Geometry structure
Options structure BK
Obs
Converge? Y
Update
Solution N
Satellite radiances as input
Design and coding underway
Interface
DA system
Now, we are building the interface between CRTM and our DA system.
The interface mainly needs to complete two pieces of work: one is loading the CRTM coefficient data files. The other is delaring and filling four structures.
Atmospheric structure is filled with the atmospheric state along the scanning line. The atmospheric state includes the distribution of hydrometeors and aerosols.
Surface structure is filled with the surface characteristics in each FOV.
Geometry structure is filled with the locations of sensors and sources.The source is typically the sun or moon.
Options structure allows users to choose the certain kind of scattering RTM, to enable and disable the antenna correction and so on.
After all the above setup, CRTM is called. Simulated radiance and gradients are produced by its forward model and k-matrix model respectively.
Then, simulated radiance and gradients are used for the minimization of the cost function. If convergence is reached, we get the solution; else the control variables are updated and the iterative loop continues.