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 9/21/2015 1610UTC

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)

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

TRMM / GPM

First sequence of steps in cloud analysis

Second sequence of steps in cloud analysis

Status of Simulations TRMM case has been selected (July 26, 2013 0000UTC) 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

Radar Reflectivity Space Based Model Initialization 15 minute forecast

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

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

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

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

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

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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

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

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

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

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

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.