Validating Satellite Products and WRF Model Microphysics with CloudSat Andrew Molthan SPoRT SAC Tuesday June 12, 2007

Introduction Satellite products such as cloud top and separation pressures are developed locally for operational use. GOES Imager, GOES Sounder, MODIS cloud top pressures. AIRS cloud top and separation pressures. The WRF Model is being run in an operational, real-time forecast mode at NSSL with explicit microphysics at high (4km) resolution. The recent launch and availability of CloudSat Cloud Profiling Radar data provide an opportunity to validate these data sets. Text slide – paraphrase high points and move on.

Leveraging The A-Train Use of the A-Train with SPoRT: AIRS radiances and soundings (Will & Brad), AIRS cloud top pressures (15km), MODIS cloud top pressures (1km) compared to GOES imager cloud top pressures (4km), GOES sounder (8km) CloudSat flies in close proximity in time and track to Aqua in order to obtain vertical profiles of clouds that are nearly coincident in time and space with MODIS, AIRS and other A-Train components. CloudSat 2B-GEOPROF documentation gives 2.5km along track and 1.2km across track resolution, radar range gate resolution of 250m. CloudSat weakness – lack of detection of high thin cirrus – is supplemented by CALIPSO lidar instrument. CloudSat also has difficulty with boundary layer clouds.

Example CloudSat Parameters dBZ Cloud Type Ice Phase Percentage Droplet Effective Radius Ice Effective Radius Will briefly discuss each of these products. Reflectivity – the “raw” output, radar return, but this has been cleaned up through a masking process that retains only returns with a high confidence of being cloud. Cloud Type – determined based on the altitude of the radar return and microphysical characteristics obtained by ECMWF model temperatures. Effective Radius products – retrievals based on microphysical character that describe number concentration and particle size. Droplet Number Concentration Crystal Number Concentration

Satellite Products Validation Cloud top pressures are derived from the MODIS, GOES Imager, GOES Sounder, and AIRS and are delivered to the web for operational use. CloudSat will provide validation of these products through CTP estimated from radar reflectivity. MODIS CloudSat GOES Imager GOES Sounder These images represent products distributed by SPoRT via the web and are all related to the same case study – T.S. Ernesto off of the tip of FL. These pictures display… An example of the products being distributed and some variation among resolutions – MODIS shows much more detail in the southeast w/scattered low level clouds. The much smaller coverage of CloudSat The ability to take a CloudSat flight track and draw out information from other geolocated satellite products. Not shown but to be mentioned: Will McCarty’s work with AIRS for CTP and separation point pressures. We have these available in a different format for assessment.

Satellite Products Validation AIRS CLOUDSAT GOES IMAGER GOES SOUNDER MODIS Probably an entire talk in this image. First, this is an example of CloudSat sampling a pretty significant “weather event”. The tropical cyclone has several banded cloud structures present and was sampled just to the right of the circulation center. CloudSat cloud top pressures are estimated (simply) here by finding the highest radar return in the column. Compare/contrast the CTP products and point out how they relate to the product derivations. In general, good agreement north of 23N where clouds are very deep and uniform. MODIS might be picking up some thin cirrus at 25N that may affect the CTP estimate although CloudSat can’t detect it. Therefore, CALIPSO lidar would be of some utility here. Sensors with lower resolution are affected by partial cloud filling – look at ~19N with GOES Imager and in the vicinity of 22N. Very, very small clouds near 28N also cause huge variations. Acknowledge that the assessment routine must be tuned – the first radar bin may not be a realistic assumption and the nearest neighbor approach duplicates the same product value for multiple radar profiles. 19N August 29, 2006 T.S. Ernesto South Florida 27N

Future Plans Investigate strengths and weaknesses of the cloud top pressure retrievals from various instruments. Resolutions differ among instruments, so careful sampling must be done to obtain accurate statistics. Incorporate the use of CALIPSO data to check for thin cirrus that CloudSat may not detect. Providing error statistics and strengths or weaknesses will assist the end user with proper application of each data set. Text slide – will just hit high points.

Looking Below the Cloud Top Assessment of cloud top pressure deals only with the upper altitude returns from CloudSat. The availability of two dimensional cloud profiles provides an opportunity to assess the simulation of clouds in high resolution NWP such as the real-time CONUS 4km WRF forecasts produced by NSSL. Trying to blend the two targets – NWP clouds and cloud tops by pointing out that while CTP only assesses the a very small amount of radar profiles, the rest of the radar data can be used towards understanding the accuracy of cloud resolving model simulations.

WRF Microphysics Validation WRF 4km Forecast Model Locate CloudSat Track Extract NWP Variables This is a “picture representation” of the steps taken thus far to make assessments using WRF and QuickBeam. We are obtaining the WRF 4km Forecast Model at CloudSat overpass times through our partnership with NSSL and in particular the work of Scott Dembek. We can locate a CloudSat area of interest through the CloudSat DPC Quicklook imagery or by using the NRL NexSat web page. After the CloudSat 2B-GEOPROF data are available we can use a nearest neighbor approach to extract NWP variables relevant to QuickBeam: altitude through geopotential, hydrometeor mixing ratios, temperature, pressure, etc. QuickBeam is depicted as a “black box” since it is a second model – and we need to carefully determine and tune the parameters that are used to create size distributions of hydrometeors for the simulated radar signal to transmit through. Finally, the end result/big picture is the ability to make comparisons (subjective for now) between WRF simulated clouds (right) and CloudSat observations (left). This is an example slide for the process only, will highlight the actual case study in the following slides. QuickBeam Simulator Provide Validation of NWP Clouds by Comparison Simulate CloudSat Profiles

Preliminary Case Study March 1, 2007 This is a surface map (at 12Z, sshhh!) for the case study date used in the QuickBeam simulations. The surface fronts and precip demonstrate the synoptic overview (interesting to note the system is pretty intense – high risk severe weather for TN valley and wind/snow/rain for plains). The MODIS CTP product is on here to demonstrate the coverage and variability in the clouds for the same area. CloudSat 08:30 UTC MODIS

WRF Output Microphysics Retrieve a WRF model cross section as a nearest neighbor path along the CloudSat ground track to simulate radar reflectivity. Cloud Water Rain Water Graupel These are WRF microphysics cross sections from grid points that are nearest-neighbor to the CloudSat track. Just need to mention here that these are representative hydrometeor categories from a model with explicit microphysics (no convective parameterization scheme) and that although different schemes will produce different output, it is these categories that are supplied to QuickBeam to “model” the hydrometeors encountered by the simulated radar. Will note here that the color bars vary, and that the values were chosen merely to demonstrate the variability in the distribution of each hydrometeor class type among a cloud system. Ice Snow

Simulated Radar vs. Observation CloudSat Observations After passing the hydrometeor mixing ratios to QuickBeam and specifying size distributions for each class, the QuickBeam simulator returns a profile (ideally) that CloudSat would have seen if the model were perfect. Obviously, the model is not perfect – even if the parameters to QuickBeam and the hydrometeor size distributions were known exactly, phase errors and simulation errors in WRF would lead to differences along the same profile. The big picture “good fit” here though is that… The WRF cloud top heights are similar to observations. The banded structure to the sampled clouds (some rain and mainly snow precipitation in the plains) is apparent in WRF and CloudSat observations. Both the tilt and the horizontal dimension are similar. WRF in this case demonstrated a good fit to the observed tilt of the cold air mass cloud shield present from 45N-50N. WRF also hints at some cloud overlap that occurs ~42N and again at ~44N. WRF Microphysics Simulation

Future Plans Utilize additional CloudSat retrieval products for tuning the radar simulation and validating WRF microphysics output. Realistic simulation of clouds affects radiative transfer and precipitation processes and may benefit regional forecasts of temperature and rainfall. Talk through and hit the high points.