An Example of the use of Synthetic 3.9 µm GOES-12 Imagery for Two- Moment Microphysical Evaluation Lewis D. Grasso (1) Cooperative Institute for Research in the Atmosphere, Fort Collins, Colorado Daniel T. Lindsey NOAA/NESDIS/STAR/RAMMB, Fort Collins, Colorado (1) Corresponding author address: Lewis D. Grasso, CIRA/Colorado State University, 1375 Campus Delivery, Ft. Collins, CO Omaha, 2009

Surface observations are shown for the upper Midwest from 2000 UTC 27 June (2)

a c Observed thunderstorms over western Nebraska and Iowa are shown in this GOES µm image at 2345 UTC on 27 June White boxes indicate the region of the cloud top brightness temperatures used for histograms that will be shown shortly. (3)

Synthetic Imagery Using Numerical Cloud/Radiative Transfer Models Numerical Cloud Models: RAMS/WRF-ARW i) Two-moment cloud microphysics Mass mixing ratio and number concentration are both prognosed. ii) Two-way interactive nested grids iii) Four grids: 50 km, 10 km, 2 km (GOES-R), and 400 m (NPOESS). - Initial condition from NARR/GFS analysis - Output used as input to an observational operator to generate synthetic satellite imagery at different channels. Observational Operator -- Input is RAMS/WRF-ARW simulated data. -- Community Radiative Transfer Model (CRTM) used to calculate gaseous transmittance i) ABI coefficients obtained from JCSDA (3.9 – 13.3µm) ii) VIIRS coefficients are approximated from MODIS -- Modified anomalous diffraction theory (MADT) or look-up-tables for cloud optical properties -- Delta-Eddington formulation for IR ban -- Spherical Harmonics Discrete Ordinate Method (SHDOM) for λ <= 3.9 µm -- Output is a synthetic satellite image (radiance/T b ) at a given wavelength. (4)

Shaded region depicts location of grid three. The RAMS domain is 802 x 452 x 60; horizontal grid spacings were 2 km. RAMS microphysics: Pristine ice, Snow, Aggregates, Graupel, Hail, Rain, Large Cloud droplets, and Small Cloud droplets. Two-moments were predicted for all species. CCN specified and horizontally homogeneous. (5)

c RAMS simulated thunderstorms over western Nebraska and Iowa are shown in this synthetic GOES µm image at 0000 UTC on 28 June White boxes indicate the region of the cloud top brightness temperatures used for histograms. (6)

Something is wrong! Observed 3.9 µm GOES-12 Synthetic 3.9 µm GOES-12 1)Solar energy at 3.9 µm reflects off of ice particles. The smaller the ice crystals, the larger the brightness temperature. 2)Cooler synthetic temperatures suggest simulated ice crystals are too large. (7)

Microphysical Evaluation 1) Simulated anvils are composed of pristine ice, snow, and aggregates. - pristine ice self collects to produce snow. - pristine ice collects snow; snow collects snow to produce aggregates. - fall speed of snow and aggregates larger than pristine ice; leaving pristine as the hydrometeor at the top of a simulated anvil. 2) Two-moment prediction of pristine ice examined. - Three sources of pristine ice identified: a) homogeneous freezing of haze, b) heterogeneous nucleation of ice nuclei, and c) homogeneous freezing of cloud water. - Sensitivity test demonstrated that both (a) and (b) are unable to account for the mass and/or number concentration of pristine ice in a simulated anvil. - Homogeneous freezing of cloud water examined. (8)

Cloud Water (9) 1)Cloud water droplets develop from condensation of water vapor onto a user specified population of aerosol particles. 2)Cloud droplets ascend the updraft, reach and pass through the homogeneous freezing layer. 3)Cloud droplet mass and number are converted into pristine ice mass and number. 4)Somewhat continuous pattern of the sum of cloud water mass and pristine ice mass in vertical cross section containing homogeneous freezing layer. 5)Similar expectation for cloud water and pristine ice numbers not realized. 6)Artificial loss of pristine ice numbers; relatively large pristine ice particles. 7)Brightness temperatures at 3.9 µm too low.

(10) Prediction of cloud water 1)In previous version of RAMS, only one moment of cloud water predicted: Mass. 2)Cloud droplet numbers were specified and held constant. 3)Over production of pristine ice numbers during homogeneous freezing problematic. 4)Artificial reduction of pristine ice numbers to prevent over production. 5)In version of RAMS for this study, two moments of cloud water predicted: Mass and numbers. 6)Artificial reduction of pristine ice numbers no longer needed, see (4). 7)Discovered that the artificial reduction was still in the code. 8)Code fixed, sensitivity test of new code showed a somewhat continuous patter of both mass and numbers as cloud water froze homogeneously into pristine ice. 9)27 June 2005 simulation done over with improved results.

Improvement Observed 3.9 µm GOES-12 Synthetic 3.9 µm GOES-12 (11) White boxes indicate the region of the cloud top brightness temperatures used for histograms that will be next.

Two histograms depict brightness temperatures from three different sources: observed GOES-12 data (Obs); synthetic GOES-12 data from the original simulation (Old); and synthetic GOES-12 data from the new simulation (New). The histogram in (a) is for the thunderstorm over western Nebraska while the histogram in (b) is for the thunderstorm over Iowa. (12) NebraskaIowa

Conclusions 1)Synthetic imagery of simulated thunderstorms was used as a relatively new metric to help locate an error in the two-moment prediction of pristine ice. 2)New run showed improved 3.9 µm brightness temperatures for the storm over western Nebraska. 3)Little improvement for the storm over Iowa. Perhaps related CCN being initialized horizontally homogenously. (13) Thank you !