The Analysis of Boundary Layer Refractivity Using the CSU-CHILL RadarDavid Coates

Overview

•Background Information▫Boundary layer refractivity▫Meteorological Implications

•Algorithm Description•Progress

▫Future Work

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

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

BL Refractivity

•Empirically, the relationship between temperature, pressure, and moisture content can be described as:N = 77.6pT + 3.73 x 105 eT2

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 106

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

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 = 4rλ ∫0

Rn[x(r), y(r), z(r), t] drwhere φ is the phase shift of the pulse, r is the distance to the target, and λ is the wavelength of the transmitted pulse

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 - φref = 4rλ ∫0R[n(x, y, z, t1) – nref(x, y, z, t0)] dr

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

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

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

Refractivity Algorithm (Research Mode)

csuarch2netcdfArchivedCHILL Data NetCDF Files

Parameter Filen_calib

Target ReliabilityReference Phase

Parameter File

n_xtract

Refractivity Fields

Refractivity Algorithm (Real Time)ArchivedCHILL Datacsuarch2netcdf

n_calibNetCDFFiles Parameter File

Real-TimeCHILL DataParameter File

n_xtractTarget ReliabilityReference Phase

Refractivity Fields

n_calib.cpp main

startupget_paramsgetkeyval

get_menu_entry get_file_set build_file_list

confirm_do_calibration calib_targets find_reliable_targetsread_data_forayread_data_foray

add_search_path read_list confirm_do_reliability

n_xtract.cpp mainstartupget_paramsget_file_listread_file_list

build_file_list

wait_rt_data free_arraysget_targets read_foray_dataget_quality

dif_phase fit_phasesmean_phase_slopephase_range0do_smoothingmean_phase_slope

save_infocompute_test_factors

generate_productsgenerate_full_n_prodwrite_textget_stationwrite_data_foray

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

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

Algorithm Description

Bad Target Reliability Good Target ReliabilityTgt. Rel. 185900 to 182729 Tgt. Rel.

20100602

Algorithm Description

Bad Reference Phase Good Reference PhaseRef. Phase 185900 to 182729 Ref. Phase

20100602

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

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

Progress – Target Reliability

Progress – Refractivity Output

Calculated N = 257.9

Progress – Refractivity Output

Calculated N = 248.23

Progress – Refractivity Output

Calculated N = 253.6

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

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