ADB Assimilation Activities Porting of All-sky code into GSI (with Yuanfu Xie) Modularization of code with OO design Feed various atmospheric, aerosol & land surface inputs Use this for validation along with assimilation? HRRR Alaska Retro run Develop work flow / XML script Takes a delayed real-time run Runs without radar (control) and with radar (test) Good domain to test all-sky synthetic imagery Use this domain to derive visibility from camera images

Alaska no-radar run (Nov 10, 2016)

Alaska with-radar run (Nov 10, 2016)

Alaska run (Jan 26, 2017) real-time with radar no radar

Alaska no-radar run (Jan 26, 2017)

Alaska with-radar run (Jan 26, 2017)

Alaska Cloud Verification (3000’ ceiling)

Alaska Cloud Verification (any cloud)

Alaska Cloud Verification (3000’ ceiling)

Alaska METAR Temp Verification

Alaska METAR Dewpoint Verification

Alaska HRRR radar assimilation?

Alaska Radar Run Comments Real-time run may not be assimilating radar data ERROR messages in radar stdout files DPQC radar files being used, archive just has QC files Only difference is removing ‘obs/reflect’ radar directory Namelists are being kept the same NCL graphics are missing the map overlay GRIB output with Theia UPP version has incorrect projection in header Only a subset of the domain gets plotted

Alaska RUA / HRRR cloud comparison

Alaska RUA / HRRR Sim-IR comparison

Alaska RUA / HRRR ceiling comparison

Alaska RUA / HRRR cloud-top comparison

Recent Feedback from Alaska Unforecast Icing conditions are being reported Wind direction driving mountain waves Sufficient VV for -30C supercooled cloud liquid Identify case date to look at RAP and possibly rerun HRRR

ADB Assimilation Activities Simulated Visible Satellite Code is being modularized for porting to UPP Fast running approach Useful for qualitative or semi-quantitative evaluation All-Sky software improvements Nighttime – including city lights and airglow (with CIRA) Viewing from above the atmosphere (DSCOVR comparisons), starting to read in FIM 3-D cloud fields High aerosol optical depth (AOD) cases Consider Monte-Carlo techniques (e.g. PSD package)? Data Display Potential use of “on-the-fly” web viewing interface?

EMB Assimilation Activities Variational Cloud Analysis work? Consider concepts for blending satellite + METARs + First guess Vertical partitioning of hydrometeors Addition of radar data What satellite data can be used? Cloud-top pressure (existing) CLAVR-X (also includes CWP, optical thickness) Visible satellite (albedo) GOES-R cloudy radiances (with CRTM), for 3D and 4D analysis Following slides summarize earlier var cloud work in FAB

Variational Cloud Analysis Modular variational cloud analysis currently under development Based on existing LAPS and GSI cloud analyses Simultaneous solution allowing merging of all data sources 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 for validation and as input data Establish testbed evaluating OO variational cloud analysis using constraints - augmenting ensemble methods Assess physical and statistical constraints Compare various approaches for GSI inclusion

Datasets of Global Interest Geosynchronous IR / Visible GOES / GOES-R / Meteosat / Himawari Polar Orbiting JPSS VIIRS (note nighttime / lunar capability) GPM Constellation Active Radar + Microwave Microwave is available on more satellites and can be calibrated using the colocated radar on the GPM core satellite Lightning Forward model (e.g. for upward graupel flux) Consider lightning is more predominant over land

All-sky Camera Assimilation More cameras, via NOAA’s observing systems? Add to ASOS? FAA camera networks (e.g. Alaska) Airborne cameras? CSTAR / AWIPS Data assimilation with variational cloud and GSI analysis Efforts underway to use GSI cloud/hydrometeor analysis (used in HRRR/RAP) with all-sky forward model for nowcasting. Use derived METAR, cloud mask, image correlation, or spectral radiances Check applicabilty of available RTMs

All-sky Camera Assimilation Camera image on the left. Cloud mask on the right is being interfaced with the GSI for assimilation.

All-sky Camera Assimilation Clear sky simulated image panorama generated by GSI. A cloud mask associated with this forward model will be compared with camera cloud masks to clear out clouds. Clouds can be added as part of a variational analysis. These images (together with spectral radiance values) can also be used to evaluate analysis/forecast cloud 3D placement, microphysics, and radiation.

Visibility Visibility Measurement Contrast between land and sky (on the horizon) Contrast of features on the land scape (e.g. a runway) Extinction coefficient (α) (related to visibility, AOD and aerosol scale height, as well as other hydrometeors) Visibility = -ln(.05) / α Measured with a transmissometer Contrast is sensitive to both extinction “out-scatter” and “in-scatter” or airlight due to aerosols, gas, hydrometeors Simulated imagery provides 2-D radiance field over a 360 degree spherical FOV to help interpret these phenomena in camera images.

Visibility Visibility Measurement (cont) Note that AOD can be inferred variationally from clear sky radiance and 3-D extinction coefficient can in principle be constrained from calibrated camera images of the entire sky & landscape. Simpler retrievals can also be considered. Link between extinction coefficient and AOD (assuming exponential decay of aerosols with height) α = AOD / scale height

Distance to Terrain (km) Visibility 0.1 1 10 100 Distance to Terrain (km) Possibly help augment LL edge detection algorithms.

Ceiling is low obscuring the terrain with simulated clouds Visibility Clear Sky Low Clouds Ceiling is low obscuring the terrain with simulated clouds

Full Disk Earth View Earth global view SIMULATED (FIM 00h fcst) OBSERVED (DSCOVR) Earth global view Compare with DSCOVR or Himawari

Variational Cloud Strategies Choose Object / Fwd Model for Satellite / Radar Obs Constraints Physical Statistical OR OR OR Water Vapor RH & Q Saturation Q + other state vars 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, LWP, Aerosol Retrievals (e.g. CLAVR-x) IR B-temp VIS Albedo Goddard Satellite Simulator CRTM

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.