1 / 16

Robust Dual Frequency Radar Profiling Algorithm

Robust Dual Frequency Radar Profiling Algorithm. Mircea Grecu 1 , Lian Tian 1 , and Simone Tanelli 2 GEST, UMBC and NASA GSFC Jet Propulsion Laboratory. Outline. Motivation General considerations Solution Generalized HB approach Ensemble methodology Results Conclusions. Motivation.

missy
Download Presentation

Robust Dual Frequency Radar Profiling Algorithm

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Robust Dual Frequency Radar Profiling Algorithm Mircea Grecu1, Lian Tian1, and Simone Tanelli2 GEST, UMBC and NASA GSFC Jet Propulsion Laboratory

  2. Outline • Motivation • General considerations • Solution • Generalized HB approach • Ensemble methodology • Results • Conclusions

  3. Motivation • Drop size distribution variability is the main source of uncertainties in reflectivity precipitation relationships • Even when multiple observations are available, radar-only retrievals are uncertain • For small drops the equations are identical • Reflectivity factors may be subject to severe attenuation

  4. Example of Dual Frequency Airborne Radar Observations

  5. GPM challenges • Single frequency radar retrievals have to be consistent with dual frequency radar retrievals • Forward attenuation correction tends to be the method of choice in single frequency retrievals, while reverse attenuation correction is popular in dual-frequency retrievals • Radar retrievals have to be consistent with combined radar/radiometer retrievals • Radar retrievals algorithms need to be computational fast to be included in combined methodologies

  6. Outline • Motivation • General considerations • Solution

  7. Candidate solution • Generalized Hitschfeld Bordan profiling methodology can be applied to derive generic Ku (13.8-GHz) radar retrievals as a function of various parameters (e.g. vertical N0 profiles) • These parameters are optimized as a function of Ka (35.5-GHz) radar observations where these are available.

  8. Generalized HitschfeldBordan profiling

  9. Incorporation of Ka band information into the Ku solution • Variational based methodology is used • Observation vector Y=[ZmKa, PIAKa, PIAKu] is from X=[N0] using the HB algorithm and cost function F(X)=0.5(Y-Yobs)TR-1(Y-Yobs)+0.5(X-XB)TB-1(X-XB) is minimized using a gradient based procedure • Grad(F(X)) is calculated using the adjoint formulation

  10. Outline • Motivation • General considerations • Solution • Generalized HB approach • Variational methodology • Results

  11. Synthetic data experiment • Ka observations were simulated from TRMM Ku observations for randomly assumed N0 profiles • DSD shapes were assumed known • TPW and cloud water profiles were assumed known • Random errors (1dB standard deviations) were included into the Ka observations

  12. ResultsLiquid Water Content Single Frequency Retrievals Dual Frequency Retrievals C=0.92 RRMS=0.52 RBIAS=-0.05 C=0.80 RRMS=0.73 RBIAS=-0.15

  13. ResultsDrop Size Distribution Intercept Dual Frequency Retrievals Single Frequency Retrievals C=0.89 RRMS=0.73 RBIAS=-0.01 C=0.62 RRMS=0.87 RBIAS=-0.02

  14. Results Application to APR-2 data from TC4

  15. Outline • Motivation • General considerations • Solution • Generalized HB approach • Ensemble methodology • Results • Conclusions

  16. Conclusions • Variational methodology appears to be robust and perform satisfactorily • The methodology facilitates the development of combined radar radiometer retrievals • It is currently incorporated in a variational combined radar/radiometer framework • The impact of cloud water, TPW, phase transition and DSD shape uncertainties needs to be rigorously assessed.

More Related