Richard T. McNider Atmospheric Sciences Department University of Alabama in Huntsville

1. Assimilation of Satellite Cloud Products to Improve Radiative Characterization Richard T. McNider Atmospheric Sciences Department University of Alabama in Huntsville mcnider@nsstc.uah.edu

2. Use of Satellite Data to Improve the Physical Atmosphere in Air Quality Decision Models NASA Air Quality Applied Science Team Project Physical Atmosphere Panel Meeting April 25-26, 2012 Atlanta, GA

3. Physical Atmosphere Can Significantly Impact Atmospheric Chemistry and Resulting Air Quality Most Importantly the Physical Atmosphere Can Impact Control Strategy Efficacy and Response Clouds Temperature Winds Mixing Heights

4. Clouds Temperature Insolation Mixing Heights Emissions Photolysis J (NO2) Deep Vertical Mixing Boundary Layer Venting Aqueous Chemistry Aerosol Formation and Aging

5. Isoprene flux (nmoles m-2 s-1) Isoprene flux (nmoles m-2 s-1) 60 40 20 0 Light intensity (μmoles m-2 s-1) Light intensity (μmoles m-2 s-1) Light Intensity White oak leaves Saturation point a) b) * a) Lerdau et al. (1997), b) Fuentes et al. (2000)

6. One of the things geostationary satellites do best is observe the reflectance of clouds 0.65um VIS surface, cloud features

7. Also can measure cloud top tempertaure/height 10.7um IR sfc/cloud top temperature

8. Use of Daytime Cloud Albedo/Cloud Top Temperature for Model Evaluation Model Cloud Top T Model Cloud Albedo 0.65um VIS surface, cloud features 10.7um IR sfc/cloud top temperature

9. Areas of Underprediction/Overprediction can be identified for Correction Underprediction Overprediction Areas of disagreement between model and satellite observation A contingency table can be constructed to explain agreement/disagreement with observation

10. So – What can we do to improve clouds in air quality studies? One path is to simply replace model radiative properties with satellite observed properties. Insolation (McNider et al 1995 Int. J. Remote Sensing) Photolysis (Biazar et al. 2007 J. Geo. Res.)

11. Insolation– Satellites can do an excellent job in providing insolation if care is taken to develop a consistent surface albedo. Cloud albedo, surface albedo, and insolation are retrieved based on Gautier et al. (1980), Diak and Gautier (1983). SUN c h g g Surface

12. Satellite Derived Insolation

13. Baseline WRF/CMAQMethod Photolysis Rates Satellite Method Cloud top Cloud top Determined from satellite IR temperature Determined from model LW = f(RH) Transmittance = 1- reflectance - absorption Transmittance transmittance Determined from LW= f(RH) and assumed droplet size Observed by satellite F(reflectance) Cloud Base Cloud Base Determined from LCL Determined from model LW = f(RH)

14. Photolysis Rates – Errors in photolysis rates can change response time of ozone production and change significantly levels at a given monitor

15. 70 35 0 -35 Maximum Difference in Ozone due to Satellite Photolysis Fields -70 With Satellite Observations Without Satellite

16. Largest Surface O3 Differences Due to Cloud Errors - August 2006 (SatCld-Cntrl)

17. Dallas

20. Model JNO2 Satellite JNO2 Dallas Dallas Photolysis rates at first model layer for August 21, 2006, at 1200LST

21. Satellite JNO2 Model JNO2 Photolysis rates at first model layer for August 21, 2006, at 1400LST

23. While insertion of radiative properties (insolation and photolysis) can improve model performance the simple replacement of radiative properties means that we have an inconsistency in that radiative clouds are displaced from cloud water and cloud mixing in the model. PBL Model WithClouds PBL Model Without Clouds Courtesy Wayne Angevine NOAA CO profiles from P3 upwind, over, and downwind of Nashville (symbols)

24. One solution - Insert satellite derived cloud water into model (Lipton and Modica 1999 MWR) In general clouds need positive vertical velocity to exist. Thus, insertion of liquid water into the model where it does not support clouds (low relative humidities and negative velocity) means that the water quickly evaporates. In fact it can hurt model performance. If a model does not have clouds at a location that the satellite has clouds then the model likely has negative vertical velocity. Inserting the water and then having it evaporate will mean even greater downward motion which is the opposite of the satellite reality. Insertion of satellite derived liquid water Sinking Evaporation Model Low RH and W<0

25. This looks like a perfect job for 4DVAR - simultaneously adjust moisture, winds, and cloud liquid water to dynamically and thermodynamically support the cloud using linear forward models. However, a survey of the state of science reveals a lack of success in developing 4DVAR for clouds except for simple representations in very coarse grid models. This is in large part because cloud processes and cloud initiation are highly non-linear. This is further exacerbated by the fact that clouds in models are highly parameterized and the coded relationships have many conditional and on-off switches which make developing the required inter-parameter relationships difficult if not impossible (Mu and Wang, 2003).

26. Our first view comes from Meteorology 101 clouds have to have positive vertical motion to exist. W<0 W>0

27. The percentage of cloudy condition where vertical motion is positive is ~ 70% Cloud albedo W > 0 Graphical depiction of model relationship for cloudy condition between cloud albedo and vertical velocity

28. The percentage of time clear skies have negative vertical motion is greater than 60% Cloud albedo W < 0 Graphical depiction of model relationship for clear condition between cloud albedo and vertical velocity

29. Statistical VARLook for statistical relationships in model to specify target W A scatter plot of cloud albedo versus cloud water small positive cloud water are associated with a wide range of cloud albedo a scatter plot of cloud water versus W max even slight vertical velocities are related to a wide range of liquid water. By stratifying and scale selection were able to build multi-regression model to estimate a target w.

31. Statistical VAR Develop statistical relationships between model cloud albedo and model vertical velocity and RH to define a target vertical velocity. Apply target vertical velocity where satellite has clouds and model does not. Apply negative vertical velocity where satellite is clear and model has clouds. Use variational analysis to minimally adjust model horizontal components (u,v) to meet target vertical velocity at cloud top (from satellite) Solve Poisson type equation by SOR to ensure mass is conserved globally. Nudge new horizontal wind field into the model.

32. Satellite Insolation Model insolation after cloud assimilation Model Control Insolation

33. Contingency Table We need metric for determining improvements in model clouds NC: No Clouds C: Clouds Agreement Index (AI) is the sum across the diagonal

34. Agreement Index = (# of cloudy/clear grids in agreement) / (Total # of grids) Under-prediction Over-prediction Created clouds Needs refinement Removed clouds AI for WRF_cntrl AI for WRF_assim

35. WRF (cntrl) Calbedo GOES Calbedo SNAPSHOT Date: August 13th , 2006 at 19 UTC WRF (wind nudging) Calbedo

36. Agreement Index = (# of cloudy/clear grids in agreement) / (Total # of grids) Assimilation Control Agreement index increased by 7-10%

37. Analytical Approach: Under-prediction • Cloud top is known from GOES • Search the column (from top) for the air parcel that can be lifted to saturation. • Given a fixed time period (30 min), estimate the target vertical velocity. • Use 1-D variational technique to estimate horizontal wind components. • Nudge the model winds.

38. Analytical Approach: Over-prediction • Model cloud properties are known. • Estimate the height needed to reduce RH below 100%. • Given a fixed time period (30 min), estimate the target vertical velocity (subsidence). • Use 1-D variational technique to estimate horizontal wind components. • Nudge the model winds.

39. W > 0 RH > 95 Cloud albedo W < 0 Cloud albedo RH < 95 Graphical depiction of model relationship between cloud albedo, vertical velocity, and relative humidity There is no functional relationship of clouds with meteorological variables, but the sign of vertical velocity is relevant to cloud fields.

40. Typical Schemes used to infer moisture availability

41. Models have attempted to improve performance by developing improved land use classes (LUC) using in situ and satellite data

42. Unfortunately models don’t use land surface classes directly. Physical parameters such as heat capacity, canopy resistance, surface moisture have to be defined for the Land Use Class

43. We have taken a different approach and have embraced simple models but highly constrained by observations. Moisture, Heat Capacity, Solar Radiation from Satellite Observations

44. MM5 Landuse Heat Capacity MM5 Landuse Moisture Availability Surface Energy Budget Three Uncertain Parameters Evaporative Heat Flux Bulk Heat Capacity Net Short-wave radiation obtained from Satellite

45. Sensitivity of Surface Energy Budget to Various Parameters Thermal Inertia Moisture Availability Taken from Carlson (1986) to demonstrate the sensitivity of the surface energy budget model. Each panel represents the sensitivity of the simulated LST to uncertainty in a given parameter

46. Assimilation performed between 1300-1400 UTC 13Z 14Z 12h fcst 24h fcst 36h fcst 12Z 00Z 12Z 00Z Assimilation Period Free Forecast Model too dry • Assimilate: • Land Surface Temperature Tendencies computed from hourly images. • Solar insolation Model too moist LST Rising in Morning Upwelling Radiation

47. Can retrieve and analytically calculate the surface moisture that makes model temperature rate of change equal to satellite observed temperature change.

48. Satellite Observation Assimilation Control

49. Determining Bulk Heat Capacity Model Energy Budget Satellite Energy Budget Derived Heat Capacity

50. Assimilation performed in early evening 12h fcst 17LST 19LST 24h fcst 36h fcst 12Z 00Z 12Z 00Z Assimilation Period Free Forecast Model heat capacity too large • Assimilate: • Land Surface Temperature Tendencies computed from hourly images. Model heat capacity too small LST Dropping in evening Upwelling Radiation