SCENARIO

1. Elevation and Climatological Dependence of the SSI Capabilities to Discriminate Atmospheric Propagation Conditions Ada V. Bosisio 1, Ermanno Fionda 2, Piero Ciotti 3, Antonio Martellucci 41) CNR\IEIIT, Milano, Italy; bosisio@elet.Polimi.It2) Fondazione Ugo Bordoni (FUB), Roma, Italy; efionda@fub.It3) Dept. Of Elect. And Info. Eng., Univ. Of L’aquila, L’aquila, Italy; piero.ciotti@univaq.It4) ESA-ESTEC, TEC-EEP, Noordwijk (NL); antonio.martellucci@esa.Int

2. SCENARIO • Microwave Propagation Phenomena • Key parameter: Frequency (f) • O2, Water Vapor emissions: (Clear Sky) • Cloud emissions: relevant at f > 15 GHz • Rain events: relevant at f > 10 GHz • Snow and Ice, not relevant below f = 30 GHz

3. MOTIVATION Rain events can be source of strong scatter processes extremely severe for space communications at K/Q/W band For satellite communications purposes, the knowledge in real time of rain events, in the volume of the operative beacon, could suggest the adoption of dynamic fading mitigation techniques to overcome the lost of signal quality For Water Vapor and Cloud Liquid retrieval, the availability of a user-friendly tool (able to detect microwave radiometric observations affected by rain) is welcomed in case of massive measurements (radiometric-networks) or for analyzing large database

4. Candidate Indicator (SSI) • Indicator should be generated from ground-based brightness temperature values Tb(f) • It could benefit from the different sensitivity of the microwave radiometric channels to the various atmospheric constituents Candidate: • The Sky Status Indicator (SSI) issues from the ratio between available Tb(f) @ 23.8, 31.4 GHz

5. outline • Sky Status Indicator • SSI features • SSI computation • Clear sky Tb(f) and calibration check • Sensitivity analysis on SSI coefficients (synthetic data) • elevation angle and site (climatology & latitude) dependence • erroneous calibration procedure and/or apparatus uncertainty • Sensitivity analysis on SSI coefficients (measured data) • elevation angle dependence • SSI classification results • Final remarks

6. SSI features • ASSUMPTIONS The ratio T31/T23 of concurrent ground-based radiometric data depends on the thermodynamic state of the atmosphere. • This ratio detects the status of the sky along the path as it neutralizes the contribution of the water vapor plateau (continuum) and of the dry gases by defining a modified brightness temperature T31 value: • The coefficient c0is frequency-, location-, and elevation-dependent. • It is computed either from measurements or from simulation data. • Specifically, c0is the intercept of the straight line that relates the couple (T31,T23) of values under clear sky condition

7. RAOBs Forward RT Tb(f),IWV, LWP SSI computation The key point is c0 calculation, i.e. the identification of the linear fit between T23 and T31 under clear sky conditions • from simulated Tb(f): • from measured Tb(f):

8. Clear sky Tb and calibration check

9. Sensitivity analysis on ssi coefficients • RAOBs database + forward RT model including: • Rosenkranz absorption model (water vapor) • Mattioli et al. (cloud model) • Classification criteria • 3 sites (De Bilt, Roma, Milano) • 5 elevation angles q= 27.6°, 35.5°,40.2°, 69.6°, 90° The sensitivity analysis aims at assessing the dependence of ci coefficients on the elevation angle and on the climatological region. INPUT

10. RAOBs and simulated Tb(f) database • Classification criteria according to the outcome of cloud liquid model • Clear sky conditions: LWP <0.001 cm • Cloudy sky conditions: 0.001< LWP< 0.07 cm

11. Sensitivity analysis on ssi coefficients Climatology-latitude joint effect Observed variability @ 90° : 8%

12. Noise and improper calibration effects Robustness is evaluated on simulated Tb(f) • Tbn(f)=Tb(f) +N(0, s2 ) • N is AWGN with s = 0.5 K. • It accounts for the instrument radiometric resolution. • Tbne(f)=Tbn(f) ± i where i=1,2 K • The bias reproduces measurements under improper calibration periods

13. Noise and improper calibration effects • Sensitivity due to ± 1 and ± 2 of about ±6% and ±12 %

14. RAOB Experimental field in Cabauw (NL) ESA ATPROP 22 km

15. Sensitivity analysis on ssi coefficient • the T23 domain ranging from its minimum value to 50 K is divided in 200 bins • for each bin the minimum T31 value is selected • linear fit over the selected couples T23- T31 discrepancy from 6% to 9% for co and of about 1% for c1

16. SSI Probability density functions

17. SSI classification results: Cabauw(NL) SSI range variability and boundary threshold values with respect to the clear, cloudy and rainy sky conditions. The SSI values are referred to brightness temperature values measured in Cabauw during 2009.

18. Tb(15.3) as possible rain detector tool

19. Tb(15.3) during rain events

20. Final remarks: results and outlook • SSI has easy software implementation and online performance capability • Clear/cloudy and cloudy/rainy sky conditions were discriminated by two SSI boundary threshold values: 0.39 (at q equal to 90° and 69.6°) and 0.86 or 0.88 (at q equal to 90° and 69.6°). • A robustness analysis on SSI considering simulated Tb AWGN with s = 0.5 K and a bias of ±1 and ±2 K: • Sensitivity of about ±6% and ±12 % • Precipitation prediction through plane parallel rain slab to extend classification criteria to rainy sky conditions • Database of meteorological information such as rain gauges and/or radar data for experimental validation