1. The Analysis of Boundary Layer Refractivity Using the CSU-CHILL Radar David Coates

2. Overview Background Information ▫ Boundary layer refractivity ▫ Meteorological Implications Algorithm Description Progress ▫ Future Work

3. Refractivity Refractivity is an optical phenomenon in which light changes its speed and orientation upon changing mediums The relationship between speed and orientation of a ray of light in a medium is given by a medium’s index of refraction, n ▫ The index of refraction is defined as the ratio of the speed of light to the speed of the light in a given medium

4. Boundary Layer (BL) Refractivity In the atmosphere, the index of refraction depends largely on the temperature, pressure, and moisture content of the air ▫ These variables are directly related to density, and variations in each can cause large variations in air density Within the BL, temperature, pressure, and moisture content vary largely from location to location

5. BL Refractivity Empirically, the relationship between temperature, pressure, and moisture content can be described as: N = 77.6 p T + 3.73 x 10 5 e T2T2 where p is the station pressure, T is the station temperature, and e is the vapor pressure N, the refractivity, can be related to the index of refraction via N = (n-1)x 10 6

6. BL Refractivity It is impossible for radar to measure any of these variables directly, so the value of refractivity must be inferred ▫ Refraction of the electromagnetic pulses emitted from radar sites can be measured by determining the phase shift of the pulse Radar software suites have the capability to measure the refraction undergone by backscattered radiation from distant targets

7. BL Refractivity The relationship between the index of refraction of the local atmosphere and the average phase shift of a backscattered pulse is given as: φ = 4r λ ∫ 0 R n[ x(r), y(r), z(r), t ] dr where φ is the phase shift of the pulse, r is the distance to the target, and λ is the wavelength of the transmitted pulse

8. BL Refractivity Given how small the changes in the index of refraction are, distance measurements need to be accurate to the tenth of a millimeter ▫ This isn’t practical, so another method can be employed Rather than scanning targets on the fly, a calibration can be made and differences in phase can be measured: φ - φ ref = 4r λ ∫ 0 R [n( x, y, z, t 1 ) – n ref (x, y, z, t 0 )] dr

9. Meteorological Implications Current observational networks do not have a high enough resolution to make small-scale meteorological predictions ▫ Resolution issues on both spatial and temporal scales Unlike ASOS, AWOS, and other meteorological observation suites, weather radar has the capability to make small-scale measurements in rapid succession

10. Meteorological Implications Ideally, this method of measuring atmospheric temperature, pressure, and moisture fields at high resolution allows for meteorologists to make more accurate predictions Specifically, in regards to convective activity, horizontal differential thermal and moisture fields can give great insight to predicted specific locations of convection initiation

11. Algorithm Description In order to develop a reference phase, a calibration stage must be carried out After calibration, test scans are analyzed by the algorithm, which calculates and smooths the refractivity field of the surrounding atmosphere There are two modes of function: research and real-time

12. Refractivity Algorithm (Research Mode) csuarch2netcdf Archived CHILL Data NetCDF Files Parameter File n_calib Target Reliability Reference Phase Parameter File n_xtract Refractivity Fields

13. Refractivity Algorithm (Real Time) Archived CHILL Data csuarch2netcdf n_calib NetCDF Files Parameter File Real-Time CHILL Data Parameter File n_xtract Target Reliability Reference Phase Refractivity Fields

14. n_calib.cpp main startup get_params getkeyval get_menu_entryget_file_setbuild_file_list confirm_do_calibrationcalib_targetsfind_reliable_targets read_data_foray add_search_pathread_listconfirm_do_reliability

15. n_xtract.cpp main startup get_params get_file_list read_file_list build_file_list wait_rt_datafree_arrays get_targets read_foray_data get_quality dif_phasefit_phases mean_phase_slope phase_range0 do_smoothing mean_phase_slope save_info compute_test_factors generate_products generate_full_n_prod write_text get_station write_data_foray

16. Algorithm Description Separate calibrations need to be made for each season ▫ Seasonal changes in vegetation can alter size and movement of ground targets ▫ Season temperature swings can induce bias in the algorithm Attention to the surrounding surface features also needs to be taken into consideration ▫ Orographic features can results in anomalous propagation of pulses and can result in errors

17. Algorithm Description Calibration produces two products to pass into the analysis program: a target reliability diagram and a reference phase plot ▫ Special attention needs to be paid to both, as poor reliability or phase contamination can result in erroneous results

18. Algorithm Description Bad Target ReliabilityGood Target Reliability Tgt. Rel. 185900 to 182729Tgt. Rel. 20100602

19. Algorithm Description Bad Reference PhaseGood Reference Phase Ref. Phase 185900 to 182729Ref. Phase 20100602

20. Algorithm Description Analysis portion of the algorithm outputs two products: refractivity field imagery and netCDF files containing data arrays Field imagery includes averaged refractivity field, scan-to-scan differential refractivity, velocity, reflectivity, and 12-hour average refractivity change ▫ This plot can be used to determine how moisture and temperature fields change as meteorological phenomena take place

21. Progress Calibration of the algorithm was achieved using a dataset from 10 December 2010 between 1941 and 2020Z Analysis was attempted on two datasets ▫ 13 December 2010 ▫ 19 Jan 2011 Output from algorithm was compared to a nearby observation station to determine validity

22. Progress – Target Reliability

23. Progress – Refractivity Output Calculated N = 257.9

24. Progress – Refractivity Output Calculated N = 248.23

25. Progress – Refractivity Output Calculated N = 253.6

26. Progress – Status of Algorithm The refractivity algorithm currently runs without error, though there are still bugs in its computational components Possible causes: ▫Errant files in analysis datasets ▫Incorrect usage of radar constants ▫Poor approximations/bad object usage in algorithm language ▫Poor reference phase field

27. Future Work In order to ensure that the algorithm outputs realistic estimations of local refractivity fields, extensive debugging is still necessary ▫Determine the source of the erroneous calculations and fix them