1. Continuous Humidity Profiling using a Wind- Profiler Radar in the UHF band (continued) F. Saïd* (1), B. Campistron (1), D. Bengochea (1), O. Bock (2), P. Di Girolamo (3) and D. Legain (4) (1) Laboratoire d’Aérologie, Université de Toulouse, UMR CNRS 5560, Toulouse, France, (2) IGN-LAREG Paris, France, (3) Scuola di Ingegneria, Universita degli Studi della Basilicata, Potenza, Italy. (4) CNRM, Météo-France, Toulouse, France. HYMEX 9 th International Conference, Mykonos, 21-25 sept 2015 CNRM/Météo-France UHF wind profiler and radiosounding balloons (photo: BLLAST 2011)

2. -Objective  Determine (indirectly) atmospheric water vapor profiles at a fine time resolution by using a Doppler wind profiler radar (and ancillary data) -Theoretical basis -1 st step: Calibration and optimization -2 nd step: Fine resolution profiles

3. THEORETICAL BASIS : to retrieve q, the humidity mixing ratio, at level Z, we use : vertical profile of refractive index M 2, and not M, is provided by the radar P, T are provided by a RS We also need initial conditions when integrating the differential equation

4. 1 st step: CALIBRATION and OPTIMIZATION (1/4) Initial value from RS radar reflectivity reflectivity maximum (usually a transition between ≠ humidity conditions) Novelty (vs litter.)  2 integrations down and up to the Zi level.  varying calibration coefficients from one profile to the other Zi level

5. 1 st step: CALIBRATION and OPTIMIZATION (2/4) Indetermination on M sign  use the RS sign for M Before After M>0

6. 1 st step: CALIBRATION and OPTIMIZATION (3/4) q larger than the saturation threshold  limitate profiles to q sat Before After saturated mixing ratio q sat (from RS)

7. 1 st step: CALIBRATION and OPTIMIZATION: results (4/4) Bias (q RS – q radar ) and standard deviation before and after the improvements (October 2012): std decreases a lot;|bias| is the same and shows a weak underestimation from the radar at mid-levels. Before (28 profiles) After the radar is 1 g/kg larger than the RS

8. 2 nd step: FINE RESOLUTION PROFILES (1/5) RS1+ RS2 or RS1+RS3 provide RS1 RS2 RS3 q initial conditions calibration coefficients P and T profiles M sign ↓ that are interpolated 15-min RADAR obs provide Cn 2, Zi, windshear, ε RADAR 15 min q P R O F I L E S

9. 2 nd step: FINE RESOLUTION PROFILES (2/5) RS1 (15h) RS2 (21h) saturated profiles 15-min radar profiles RS1 RS2 15-min lidar profiles (BASIL) interpolated RS profiles The radar shows a cloud between 16-18h (2000-2500m) that appears and vanishes between the 15h and 21h RS. The lidar signal is attenuated above 2000m, where saturation starts.

10. 2 nd step: FINE RESOLUTION PROFILES (3/5) RS1 (3h) RS2 (9h) RS1 is dry over 2000m at 3h. An advection of moist air (the wind direction has shifted) increases q between 2000 and 3000m  well seen by the radar RS1 (3h) RS2 (9h) saturated profiles 15-min radar profiles the radar is wetter here (confirmed by W) moisture advection

11. 2 nd step: FINE RESOLUTION PROFILES (4/5) RS1 (3h) RS2 (9h) saturated profiles drying RS1 RS2 15-min lidar profiles interpolated RS profiles The radar also shows the drying between the 3h and 9h profiles and the development of the boundary layer, a little more accurately that the interpolated RS (dashed lines on the right). BL development

12. 2 nd step: FINE RESOLUTION PROFILES (5/5) RS1 (3h) RS2 (9h) this part of the virga and the moistening below are well documented with the radar but are not seen by the RS.

13. CONCLUSION The retrieval of air humidity profiles from radar reflectivity is an ill-posed problem because extra parameter are needed  limit the ambitions of the profiler retrieval. Using RS at two different times, the profiler is able to retreive humidity at intermediate time (at a finer temporal resolution). In that configuration the profiler works as a temporal interpolator. The original and most important improvement: the double vertical integration (upward and downward) heading to the Zi position provided by the profiler + specific calibration coefficients Operational use  requires some refining. However, tests have to be made in different weather conditions (dry), and the lidar would be perfect to assess the results. [IWV from GPS could not help (to constrain the profiles) since their variation was not consistent with the RS variation]