noaa / NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

Office of Oceanic and 

Atmospheric Research

NOAA Technical Memorandum OAR PSD-312

SCIENTIFIC BASIS AND DESIGN OF A GROUND-BASED REMOTE ICING 

DETECTION SYSTEM (GRIDS)

W.C. Campbell

J.S. Gibson

R.F. Reinking

T.L. Schneider

Earth System Research Laboratory

Physical Sciences Division

Boulder, Colorado

July 2008

UNITED STATES

DEPARTMENT OF COMMERCE

Carlos M. Gutierrez

Secretary

NATIONAL OCEANIC AND

ATMOSPHERIC ADMINISTRATION

VADM Conrad C. Lautenbacher, Jr.

Under Secretary for Oceans

and Atmosphere/Administrator

Office of Oceanic and 

Atmospheric Research

Dr. Richard Spinrad

Assistant Administrator

NOAA Technical Memorandum OAR PSD-312

SCIENTIFIC BASIS AND DESIGN OF A GROUND-BASED REMOTE ICING

DETECTION SYSTEM (GRIDS)

W. Carroll Campbell

Janet S. Gibson

Roger F. Reinking

Timothy L. Schneider

Earth System Research Laboratory

Physical Sciences Division

Boulder, Colorado

July 2008
ii

NOTICE: 

Mention of a commercial company or product does not constitute an endorsement by 

NOAA/ESRL. Use of information from this publication concerning proprietary products or the 

tests of such products for publicity or advertising purposes is not authorized. 
iii

CONTENTS 

1. BACKGROUND ................................................................................................................................................1

1.1 INTRODUCTION ....................................................................................................................................................1

1.2 HISTORY OF ICING HAZARDS RESEARCH AT ETL................................................................................................3

2. SCIENTIFIC BASIS..........................................................................................................................................4

2.1 RADAR.................................................................................................................................................................5

2.2 DUAL-CHANNEL RADIOMETER............................................................................................................................9

2.3 TEMPERATURE PROFILING .................................................................................................................................10

2.3.1 Radar Detection of the Bright Band..........................................................................................................11

2.3.2 Temperature Profiles from Radiosondes...................................................................................................11

2.3.3 RUC Model ...............................................................................................................................................12

2.3.4 Radiometric Temperature Profiling ..........................................................................................................12

2.4 ICING HAZARD ALGORITHM ..............................................................................................................................15

2.5 GRIDS OPERATIONAL EXPERIENCE ..................................................................................................................17

2.5.1 AIRS II—Essence of Case of 11 November 2003 ......................................................................................18

2.5.2 WISP04—Essence of Case of 10-11 March ..............................................................................................22

3. DESIGN OF GRIDS ........................................................................................................................................28

3.1 SENSORS ............................................................................................................................................................29

3.1.1 Radar.........................................................................................................................................................30

3.1.2 Radiometers ..............................................................................................................................................34

3.1.3 Temperature profiles.................................................................................................................................34

3.1.4 Surface Meteorological Sensors................................................................................................................35

3.2 ON-SITE COMPUTER SYSTEMS...........................................................................................................................35

3.2.1 GRIDS Software Architecture ...................................................................................................................35

3.2.2 GRIDS Computer Architecture .................................................................................................................36

3.2.3 Radar Data Acquisition and Processing...................................................................................................38

3.2.4 Radiometer Data Acquisition and Processing ..........................................................................................39

3.2.5 RUC Model Data Processing....................................................................................................................39

3.2.6 Surface Met Data Processing....................................................................................................................39

3.2.7 Icing Algorithm Processing.......................................................................................................................39

3.2.8 XRADS User Interface ..............................................................................................................................40

3.3 ICING HAZARD ALGORITHM IMPLEMENTATION.................................................................................................40

3.3.1 Core Algorithm Implementation................................................................................................................40

3.3.2 Algorithm Enhancements ..........................................................................................................................42

3.3.3 Important GRIDS Byproducts ...................................................................................................................44

3.4 MONITORING AND CALIBRATION.......................................................................................................................44

3.5 COMMUNICATION ..............................................................................................................................................46

3.5.1 Internet......................................................................................................................................................46

3.5.2 Local Area Network (LAN) .......................................................................................................................46

3.5.3 IEEE-488 Bus............................................................................................................................................47

3.6 CONTAINER........................................................................................................................................................47

3.7 OFF-SITE COMPUTER SYSTEMS .........................................................................................................................49

3.7.1 Software Architecture................................................................................................................................49

3.7.2 Computer Architecture..............................................................................................................................50

4. SUMMARY ......................................................................................................................................................50

5. ACKNOWLEDGMENTS ...............................................................................................................................51

6. REFERENCES.................................................................................................................................................52
iv

FIGURES 

FIGURE 1. GROUND-BASED REMOTE ICING DETECTION SYSTEM (GRIDS) NEAR AN AIR TRAFFIC CENTER. ..................4

FIGURE 2. RADAR MEASUREMENT OF DEPOLARIZATION RATIO (DR) VS. ELEVATION ANGLE .......................................8

FIGURE 3. RATIOS τW (89 GHZ) / τW (30 GHZ) (BLUE) AND τW(30 GHZ)/ τW(89 GHZ) (RED) AS A FUNCTION OF 

TEMPERATURE. ...................................................................................................................................................14

FIGURE 4. ILLUSTRATIONS OF THE MEAN LIQUID WATER TEMPERATURE RETRIEVALS IN “WARM” AND “COLD”

STRATUS DURING AIRSII (FROM SCHNEIDER ET AL. 2004).................................................................................15

FIGURE 5. THE DECISION POINTS FOR THE GRIDS CORE ICING HAZARD......................................................................17

FIGURE 6. NASA TWIN OTTER 2DC-GREY PROBE IMAGERY OF ICE CRYSTALS OBSERVED AT 1635 UTC. .................19

FIGURE 7. NASA TWIN OTTER 2DC-GREY PROBE IMAGERY OF FREEZING DRIZZLE (SLD) OBSERVED AT 2031 UTC.19

FIGURE 8. TIME-HEIGHT IMAGE OF THE GRIDS’ ICING HAZARD..................................................................................20

FIGURE 9. SPECTRA OF VERTICAL VELOCITY AS A FUNCTION OF ALTITUDE .................................................................21

FIGURE 10. INTEGRATED VAPOR AND LIQUID VALUES MEASURED BY THE GRIDS RADIOMETER ................................23

FIGURE 11. LIQUID WATER CONTENT MEASURED DURING CLIMB OUT AT ~2230 UTC. ...............................................24

FIGURE 12. VERTICAL SLICE (RHI SCAN) OF THE DEPOLARIZATION RATIO (DR) AT 2002 UTC ..................................25

FIGURE 13. TIME-HEIGHT (MSL) IMAGE OF THE GRIDS ICING PRODUCT....................................................................26

FIGURE 14. HORIZONTAL AND VERTICAL CROSS SECTIONS OF CIP ICING POTENTIAL FOR 2300 UTC..........................27

FIGURE 15. SIMPLIFIED BLOCK DIAGRAM OF GRIDS SHOWING MAJOR SUB-SYSTEMS.................................................29

FIGURE 16. DIAGRAM OF RADAR IN GRIDS. ...............................................................................................................31

FIGURE 17. SOFTWARE ARCHITECTURE DIAGRAM SHOWING INFORMATION FLOW.......................................................36

FIGURE 18. CORE ICING HAZARDS ALGORITHM ...........................................................................................................41

FIGURE 19. PLAN VIEW OF GRIDS CONTAINER SHOWING LOCATION OF KEY EQUIPMENT ...........................................48

FIGURE 20. SIDE VIEW OF CONTAINER SHOWING 3-M ANTENNA. .................................................................................48

FIGURE 21. DATAREXX SERVER DIAGRAM SHOWING DATA SOURCES, SERVER, AND DATA CLIENTS............................49

TABLES 

TABLE 1. COMPARISON OF PURPOSE AND FEATURES OF KA-BAND AND S-BAND POLARIZATION DIVERSITY RADARS ....5

TABLE 2. RADAR CHARACTERISTICS OF GRIDS OPERATING AT 40° ELEVATION .........................................................32

TABLE 3. ICING ALGORITHM THRESHOLDS...................................................................................................................41

TABLE 4. ANALOG SIGNALS MONITORED BY RADAR MONITOR PROCESS ....................................................................45

APPENDICES 

Appendix A: History of Icing Hazards Research at NOAA/ETL............................................... A-1 

Appendix B: Enhancements to Core Icing Algorithm for GRIDS.............................................. B-1 

Appendix C: GRIDS Requirements................................................................................... C-1 

Appendix D: GRIDS Block Diagram and Parts List............................................................... D-1 

Appendix E: GRIDS Features and Benefits......................................................................... E-1 

Appendix F: Software Catalog......................................................................................... F-1 

Appendix G: GRIDS Software Specifications....................................................................... G-1 

Appendix H: Implementation of the Digital Receiver............................................................. H-1 

Appendix I: GRIDS Covariance Algorithms....................................................................... I-1 
Scientific Basis and Design of a Ground-based Remote Icing 

Detection System (GRIDS) 

ABSTRACT. In-flight icing of aircraft has been identified by the Federal Aviation 

Administration as a serious problem for both commercial and general aviation. NOAA’s 

Environmental Technology Laboratory1

 in conjunction with the FAA has identified and 

developed a measurement system to detect icing conditions in clouds. This Groundbased Remote Icing Detection System (GRIDS) utilizes state-of-the-art, ground-based 

remote sensors and an operational National Weather Service numerical model. The 

system incorporates four elements: a dual-polarization Ka-band radar to measure 

reflectivity and depolarization ratio of cloud hydrometeors, a two-channel microwave 

radiometer to measure cloud liquid water and water vapor profiles, another radiometer 

channel to measure the atmospheric temperature profile, and a meteorological model of 

the temperature profile retrieved via the Internet. This information is combined in an 

algorithm to measure the icing threat as a function of altitude above the radar. This 

document establishes the scientific basis of the icing algorithm, and specifies both the 

hardware and the software design of GRIDS. GRIDS makes all the required 

measurements to implement the icing algorithm, display the results locally, and 

disseminate the results via the Internet, to warn in real time of in-flight icing hazards. 

GRIDS also measures as by-products several meteorological parameters in addition to 

those required for the icing algorithm that can effectively complement and enhance localarea meteorological observations and local and mesoscale forecasts. 

1. BACKGROUND 

1.1 Introduction 

Air traffic is increasing, spatial separations are decreasing, and aircraft in-flight icing is a 

growing threat to aviation, not only in the U.S. but worldwide. According to reports prepared for 

the FAA, aircraft accidents due to icing in the U.S. alone claim 30 lives annually, injure 14 

others, and result in $96M in lost property (Paull and Hagy 1999). Icing conditions also disrupt 

air traffic operations resulting in large financial consequences for both airlines and passengers. 

Recommendations from a 1996 Federal Aviation Administration (FAA) workshop strongly 

emphasized the need to develop new remote sensors to detect Super-cooled Large Droplets 

(SLD), i.e., droplets approaching and including drizzle sizes, which can be particularly 

hazardous (Riley and Horn 1996; summarized by Reinking and Kropfli 2000). 

In-flight aircraft icing occurs when cloud droplets or raindrops that are supercooled as liquid 

to temperatures below 0°C freeze upon impact on the surfaces of an aircraft. Federal aviation 

regulations (Federal Aviation Regulation Part 25 Appendix C; FAR25-C; FAA, 1982) 

established two decades ago specify maximum tolerable icing conditions in terms of cloud liquid 

water content, cloud droplet size, and sub-zero temperatures (°C), but these guidelines consider 

only droplets less than 50 microns in diameter. Recent research has established that still larger 

droplets 50-500 microns in diameter can also present a severe hazard. These SLDs can penetrate 

 

1

 NOAA/ETL was merged into the Physical Sciences Division of the Earth System Research Laboratory on 

1 October 2005. 
2

the slip stream and freeze as rough ice on aircraft surfaces aft of the leading edge of the wing 

(Sand et al. 1984; Ashendon and Marwitz 1987; Politovich 1989; Cober et al. 1995; Ashendon et 

al. 1996, Politovich 1996). Droplets both smaller and larger than SLDs tend to spread evenly on 

the airframe and therefore cause a less hazardous coat of ice than that typically made by SLDs. 

However, all supercooled droplets that are large enough to penetrate the airstream to reach 

an airfoil present a potential icing threat. They are difficult to forecast and to detect. Currentlyavailable operational weather surveillance systems are inadequate for use in detecting icing 

conditions, because of non-optimal wavelengths, polarizations (or lack thereof), and scanning 

priorities. 

This document presents the design of a remote sensing system that overcomes such 

impediments with hardware and software that brings the focus directly to the icing hazard, and 

thus addresses the national need to help mitigate hazards caused by supercooled water in clouds. 

This design specifies a prototype Ground-based Remote Icing Detection System (GRIDS). This 

system is based on research sponsored by the Federal Aviation Administration (FAA) and 

conducted and supplemented by NOAA’s Environmental Technology Laboratory (ETL) over the 

preceding decade. The research was carried out under the auspices of the FAA’s series of 

Winter Icing and Storms Projects (WISP). During this development, GRIDS and its 

predecessors were extensively field tested and modified many times to establish a reliable basis 

for the design of this prototype, which is suitable to build for operational application on a trial 

basis. GRIDS makes all the required measurements and supporting data acquisitions to 

implement the icing algorithm, display the results locally, and disseminate the results via the 

Internet, to warn in real time of in-flight icing hazards. 

Two prestigious awards attest to the significance and potential of GRIDS to help to prevent 

icing accidents: In 2001, the NOAA/ETL Radar Meteorology and Oceanography Division was 

awarded the U.S. Department of Commerce Gold Medal “for theoretical, experimental, and 

engineering advances that led to the development of a new technology—an autonomous, groundbased, remote-sensing system to detect dangerous in-flight icing conditions in clouds,” and in 

2002 NOAA/ETL was presented the FAA Administrator’s Excellence in Aviation Award “for 

outstanding contributions to the FAA Aviation Weather Research Program.” Both of these 

awards are the result of the development of GRIDS. Schematically depicted in Figure 1 is a 

GRIDS unit supporting flight operations near an air terminal. 

The focus of GRIDS is detection of the icing hazard in a local flight operations area. This 

hazard detection can be used not only in local warnings but also as a measurement to verify and 

extrapolate to a larger area the icing forecasts from operational numerical models. In particular, 

GRIDS is synergistic with forecast models that provide a product focused specifically on aircraft 

icing. A new product under development, the Current Icing Potential (CIP) algorithm (Bernstein 

et al. 2004), is especially promising in the modeling arena and has been interactively tested with 

GRIDS (Bernstein and Schneider 2004). 

To obtain the key icing parameters, GRIDS also routinely measures a number of cloud and 

atmospheric parameters as by-products that can be used to enhance local area observations and 

forecasts. Measures of ceiling and vertical cloud structure, in-cloud turbulence, total precipitable 

water, and the atmospheric water vapor profile are examples of the several valuable by-products 

available in the GRIDS data stream. 
3

In this document we discuss the many options that were considered in designing GRIDS, the 

rationale for making specific choices, and the tested and selected design. This document serves 

as a resource and a reference for the GRIDS development team, the FAA, NOAA, and any other 

potential user of this technology. 

1.2 History of Icing Hazards Research at ETL 

Over the past two decades NOAA ETL has, in partnership with the FAA, investigated the 

use of both passive and active remote sensors to determine when icing conditions exist aloft. A 

short history of ETL’s research is outlined in Appendix A. 

ETL’s researchers have used theoretical modeling, instrument system development, and 

experimental observations to determine and refine optimal means to detect icing hazards. Each 

successive year sharpened the focus on observational methods that more confidently identify 

regions of hazardous cloud, and distinguish them from benign regions. Some failures steered the 

research to optimal technologies. ETL tried but abandoned dual-frequency (9.3 and 35 GHz) 

observations to detect and map super-cooled water in clouds, for both practical and scientific 

reasons (as detailed in Appendix A). A continued effort that focused on new radar polarization 

technologies proved successful and is described in this document. 

Note that on October 1, 2005, ETL was merged into NOAA’s Earth System Research 

Laboratory (ESRL) as part of the Physical Sciences Division (PSD) and no longer exists under 

the name Environmental Technology Laboratory. Historical references in this report, including 

reference to GRIDS development will still refer to ETL, however. 
4

Figure 1. Ground-based Remote Icing Detection System (GRIDS) near an air traffic center. 

GRIDS will operate continuously and unattended, providing automated warning of icing hazards 

within clouds, as depicted in the overlay (from Reinking et al. 2001a). 

2. SCIENTIFIC BASIS 

Based on ETL’s research, a system that consists of four primary elements has been 

developed to detect potentially hazardous icing conditions: a dual-polarization Ka-band radar to 

measure reflectivity and depolarization ratio of cloud hydrometeors, a two-channel microwave 

radiometer to measure cloud liquid water and water vapor profiles, another radiometer channel to 

measure the atmospheric temperature profile, and an operational meteorological model of the 

temperature profile retrieved via the Internet. This information is combined in an algorithm to 

measure the icing threat as a function of altitude above the radar. The following sections discuss 

these elements in greater detail. 
5

2.1 Radar 

The dual-polarization Ka-band cloud-sensing radar is the cornerstone of GRIDS. Its chosen 

operating frequency near 35 GHz (8.66 mm wavelength) is important because it is in an 

atmospheric “window” with little gaseous absorption to degrade signals. This frequency also 

allows the radar to easily detect the small cloud droplets (including SLDs) that pose a serious 

icing threat, a much more difficult task for lower-frequency radars such as the NEXRAD S-band 

(3-GHz/10-cm) radars that are used for weather surveillance. Another possible choice of 

frequency for GRIDS is near 90 GHz (W-band, ~3mm), the next higher “window.” But at 

90 GHz there remains significant (and time-varying) gaseous and liquid absorption, components 

are much more expensive, and transmitters are both less powerful and less reliable. At 35 GHz, 

the size of the antenna is more manageable for the same angular beamwidth, and sidelobes are 

reduced with respect to weather surveillance radars. Reduced sidelobes have the advantage of 

eliminating ground clutter that can overwhelm the desired atmospheric signals at short ranges. 

At 35 GHz, radar technology is relatively mature, meaning that while components are more 

costly than at lower frequencies, they are not exorbitant, and transmitter technology is robust and 

of sufficient power level. No single radar technology can be used for all weather sensing needs. 

Key features of 35 GHz technology that make it very suitable for detecting the in-flight icing 

hazard are noted in Table 1, where they are compared to features of the NEXRAD radars. The 

weather detection capabilities of these two types of radars are primarily complementary 

(compare Kropfli and Kelly 1996, to Zrnic and Ryzhkov 1999). 

Table 1. Comparison of purpose and features of Ka-band and S-band polarization diversity 

radars 

GRIDS Ka band NEXRAD S Band 

• Purpose: short-range cloud profiling • Purpose: long-range surveillance

• High sensitivity to small hydrometeors • Precipitation (large particle) 

sensing 

• Polarization tailored to detecting in-flight 

icing hazard 

• Polarization design focused on 

rain measurement and hail 

isolation 

• Acceptable attenuation at short range, 

except in heavy rain 

• No significant attenuation 

• Minimal degradation from ground clutter • Near-field ground clutter 

• Fine spatial resolution • Coarse spatial resolution 

• Minimal Bragg (clear air) scattering • Confused by Bragg scattering at 

SLD sizes 

• Transportable and portable (1 seatainer) • Not portable (some S-bands are 

transportable, in several 

seatainers) 

In its simplest configuration, the GRIDS radar measures two parameters for input to the 

icing hazard algorithm: equivalent radar reflectivity factor (Ze) and depolarization ratio (DR). 

Both are measured at a fixed-beam elevation angle of 40°, a carefully selected angle. DR is the 

primary measurement for GRIDS because it can be used to distinguish spherical from non-
6

spherical particles. Ze is also important because it is determined by the size distribution of the 

cloud hydrometeors and thus relates to the mass of water substance per unit volume contained in 

the cloud being probed. To determine Ze accurately, one must be assured of the radar calibration 

coefficients, which can drift and change. Hence it is important to routinely and automatically 

check the calibration of the GRIDS radar, which will measure clouds with reflectivities down to 

approximately -59 dBZ at a range of 10 km. At this level of sensitivity (-29 dBZ in strong 

channel, -59 dBZ in weak channel), the GRIDS radar can measure DR accurately in a 10-kmhigh cloud of mono-dispersed 20-micron droplets with a liquid water content as low as 

0.06 g m-3, or in a 400-micron droplet cloud with as little as 10-5 g m-3 liquid water content, both 

well below the level of any icing hazard. GRIDS is a Doppler radar, so estimates of the beamradial velocity, Ve, of reflecting hydrometeors are also routinely measured. Enhancements to the 

icing algorithm could incorporate Ve. 

As designed, the radar transmits purely polarized radiation and receive signals in two 

channels: one channel for the transmitted polarization (co-polar) and the other channel whose 

polarization is orthogonal to the transmitted polarization (cross-polar). Reflectivity factor Ze is 

determined from signals in the stronger channel. DR, measured in dB, is defined as a 

logarithmic ratio of the radar reflectivity received in the weaker, cross-polar receiving channel to 

the reflectivity measured in the stronger, co-polar receiving channel. DR is determined by the 

dominant shape, settling orientation, and density of the population of cloud particles from which 

the transmitted radar pulses are scattered. For particles whose geometric cross section, as viewed 

by the radar, is nearly circular, very little depolarization occurs and DR is low. In theory, 

DR = -∞ dB for truly circular targets or spherical particles. In practice, DR from such circular 

targets is determined as the radar’s low-threshold value established primarily by the imperfect 

properties of the receiving antenna and transmitter (i.e., polarization cross-talk) and secondarily 

by the scattering particles. Typical radar cross-talk values for linear and circular polarized 

signals should be on the order of -30 dB for GRIDS. 

Since ice particles typically have non-circular geometric cross sections as seen by the radar, 

they will produce depolarization ratios larger than the antenna cross-talk (except for some shapes 

like hexagonal plates, when viewed directly from below). Hence one can use DR and non-zenith 

view angles to distinguish non-hazardous ice particles from potentially hazardous spherical water 

droplets (which provide circular geometrical cross sections at any view angle). 

Figure 2 shows DR of different hydrometeors as a function of the radar elevation angle, as 

actually measured by ETL’s scanning 35-GHz polarimetric Doppler radar during the 1999 Mt. 

Washington Winter Icing Sensors Project (MWISP) campaign in New Hampshire (Ryerson et al. 

2000). There is a clear distinction between patterns of DR vs. radar elevation angle for drizzle 

(i.e., SLD if super-cooled) and these regular types of ice crystals. Irregular ice crystals (e.g., 

conglomerates) produce somewhat lower depolarization ratios than regular types of ice crystals 

(Reinking et al. 2002), but their DR values are still well above those for supercooled droplets at 

non-zenith elevation angles. 

The fixed 40° elevation chosen for GRIDS is within the 30°-45° elevation range established 

by Matrosov et al. (2001) as optimal for distinguishing cloud droplets from ice hydrometeors 

when using one fixed beam. As can be seen in Figure 2, when the radar is pointed to the zenith 

(90° elevation angle), planar-type crystals (e.g., hexagonal plates) produce DR values that are 

very close to that of water droplets (i.e., around -30 dB), because both types of particles have 

nearly circular geometrical cross sections. However, at elevation angles well removed from 90°, 
7

there is a distinct separation between the DR values for spherical water droplets and any ice 

crystal for which the geometrical cross section is now non-spherical. Very low elevation angles 

will result in a significant increase of propagation path causing loss of sensitivity for upper cloud 

layers and will introduce possible propagation effects (e.g., attenuation and phase rotation). 

 Although it is not a primary function of GRIDS to categorize cloud ice, note that ice 

particle type (most basically, columnar vs. planar, but also graupel, aggregates, etc.) and the 

evolution of the ice type is better established by measuring DR at two fixed elevation angles 

(e.g., 90° and 40°). This configuration will provide an abbreviated and economical equivalent of 

the continuous range-height (RHI) scanning that so definitively identified hydrometeors in the 

WISP experiments (e.g., Figure 2 and Reinking et al. 2002). The introduction of ice into a cloud 

volume will influence the depletion of the liquid droplets that cause aircraft icing, and the ice 

type influences and reflects the rate of that depletion. Thus, the basic GRIDS algorithm may be 

enhanced by an automated accounting of ice presence, type and evolution. Significant updrafts 

and turbulence can produce cloud liquid and influence droplet growth, so Doppler velocity and 

velocity variance measurements at vertical incidence (90°) also might improve detection of 

hazardous icing conditions, especially in mixed-phase clouds. These and other potential 

enhancements are outlined in Section 3.3, Icing Hazard Algorithm Implementation and in the 

corresponding Appendix B. Therefore, an optional and desirable capability for GRIDS is to 

transmit a vertical beam at 90° elevation angle, in addition to its primary 40° elevation 

transmission. 
8

Figure 2. Radar measurement of Depolarization Ratio (DR) vs. elevation angle for various 

regularly shaped cloud particles during the 1999 MWISP campaign near Mt. Washington, New 

Hampshire (from Reinking et al. 2002). 

The measurements in Figure 2 were obtained by transmitting linear polarization at a 

polarization rotation angle of 45° from horizontal polarization. For the purpose of the 

hydrometeor differentiation needed for identifying the icing hazard, this polarization is certainly 

superior to standard horizontal polarization used in most weather radars; the several reasons for 

this are detailed by Reinking et al. (2002). Theoretical studies that incorporate real-life “flutter” 

effects of falling ice particles (Matrosov et al. 2001, 2005) show that a circular or near-circular 

polarization can provide even greater and more stable separation in DR between ice particles and 

water drops. The collective WISP experiments demonstrated conclusively that the 

experimentally measured values of DR will closely match those calculated theoretically for 

polarization states ranging through the continuum of ellipticity, where pure circular corresponds 

to an ellipticity of unity and pure linear to zero. Although the 45° slant linear state (with slight 

ellipticity) was subjected to the most in-depth testing, circular (with slightly less ellipticity than 

unity) was also tested (Reinking et al. 1997a; Matrosov et al. 2001), and the principles can be 

merged. The introduction of ellipticity that deviates slightly from unity causes a stronger radar 

echo in the “weak” receiving channel. This will enable GRIDS to make meaningful 

measurements of even weaker reflectivity clouds. This ability comes with a price—transmitting 

elliptical polarization slightly diminishes DR separations between ice particles and water drops. 

However, this price is worth paying to achieve the enhanced sensitivity. In summation, for 

GRIDS we specify the antenna and feed system to have circular polarization within standard 
9

limits of tolerance. But it is almost certain that the delivered antenna and the transmitted state of 

polarization will be imperfect, and will therefore advantageously demonstrate a slightly modified 

ellipticity. As part of the radar calibration, it will be necessary to measure the actual transmitted 

polarization state (i.e., its ellipticity) and the antenna’s polarization cross-talk, and use those 

values to optimize the decision points in the icing algorithm. 

2.2 Dual-Channel Radiometer 

GRIDS’ primary function is the detection of the presence of liquid water (LW) in quantities 

above a meaningful threshold, and in drop sizes of 50–500 microns and smaller. For GRIDS, the 

radar is the primary observing instrument for determining cloud particle type and identifying 

clouds of droplets, but it alone cannot make all necessary observations. Also required is a means 

to remotely sense the quantity of liquid water in the clouds, and a means to determine the 

temperature of that liquid water. GRIDS offers the power added by combining remote sensors. 

Microwave radiometers passively receive microwave emissions from the volume of 

atmosphere and clouds within their “viewing” cone. From these measurements, and depending 

on the selection of receiving frequencies, tropospheric meteorological moisture and temperature 

variables can be estimated. A review of the principles of microwave radiometry has been 

published by Westwater et al. (2005, 2006). It is surprising that microwave radiometers, such as 

the GRIDS two-channel instrument, have not been deployed operationally to all first-order 

forecasting stations, because the continuously available (24 hours a day, 7 days a week) 

measured values and trends of total and profiled atmospheric vapor (precipitable water), plus the 

cloud liquid to address icing, are so fundamental to and offer so much added value to forecasts. 

This value and the added power of radiometer-radar combinations has been demonstrated for 

storm-embedded orographic gravity waves and for lake-effect storms, for example (Klimowski et 

al. 1998, Reinking et al. 2000a, Reinking et al. 1993a), and for many icing scenarios as shown by 

several of the field tests referenced in this document. 

The dual-channel microwave radiometer has a well-proven capability for estimating total 

path-integrated LW (g m-2) amount as well as the total path-integrated water vapor (WV, g m-2) 

amount (Westwater 1972; Hogg et al. 1983; Westwater et al. 2005, 2006; Martin et al. 2006). 

The technique is based on deriving the optical thickness of the atmosphere at two frequencies 

(e.g., near 24 and 31 GHz) by measuring the corresponding radiometric brightness temperatures 

and relating them to LW and WV. This is possible because, at the selected frequencies, optical 

thickness depends nearly linearly on LW, WV, and on the amount of oxygen in the atmosphere. 

The oxygen component is quite stable and can therefore be accounted for with a high degree of 

accuracy. Selection of the two frequencies is important, since the coefficients in the linear 

relations between optical thicknesses and LW and WV are frequency-dependent and must be 

determined precisely. 

Once the brightness temperatures are measured at the two frequencies, it is straightforward 

to derive the path-integrated amounts of both water vapor (of interest as a by-product from 

GRIDS) and cloud liquid water (of direct interest because this measurement helps determine the 

severity of the icing hazard). Ice in clouds does not contribute to the microwave signal in the 

frequency range below 90 GHz (Westwater et al. 2006) and does not hamper the liquid 

measurement. A practical liquid water detection threshold is approximately 0.05 mm for the 

dual-channel microwave radiometer. Recent advances in receiver technology make it practical 
10

to receive multiple frequencies sequentially, and commercial radiometer products are available 

that use this capability to improve accuracy and versatility. 

As reviewed by Reinking et al. (2000a), the microwave radiometer’s measurement of LW is 

most accurate when droplet sizes are such that their scattering is in the Rayleigh regime (small 

compared to the radiometer wavelength). As droplet diameters exceed about 100 microns, the 

radiometric retrievals begin to break down. However, the degradation is not significant unless 

considerably larger droplets occur. The measurement is not significantly degraded by drizzle on 

the antenna, even though the drops can be several hundred microns in diameter. A uniform 

water coating or sprinkling of drops on the antenna will not degrade the measurement. 

Since a microwave radiometer passively receives the emissions from the clouds, its signal 

reception is degraded when the rain rate is sufficient to cause rivulets on the antenna. This 

degradation is minimized by using a spinning-disc antenna or an integrated blower system to dry 

a viewing window. The latter is employed in commercial versions. Meteorologically, many 

documented cases of severe icing, including meteorologically analyzed cases that actually caused 

aircraft crashes have happened in stratiform clouds of supercooled liquid, where the introduction 

of ice particles to initiate rain processes has not yet occurred, and where drizzle-sized droplets 

dominated (Cober et al. 1995; Politovich 1989; Ashendon et al. 1996; Ashenden and Marwitz 

1997). In such clouds, GRIDS will readily measure the hazard. The radar-radiometer 

combination also will readily identify glaciated clouds, i.e., those with insignificant liquid and no 

icing hazard. 

Icing has been encountered by research aircraft in mixed-phase—liquid plus ice—clouds, 

some producing rain (Riley and McDowell 1998). Statistically, the liquid fraction increases with 

temperature but has a minimum around -15°C, where competition for ice crystal growth is a 

maximum (Boudala et al. 2004). Also statistically, in situ measurements have shown that there 

is no correlation between ice and liquid content in mixed-phase clouds at a given temperature; 

however, the same experimental evidence also indicates that ice and liquid in natural clouds can 

be well separated in space and located in single-phase clusters, and that such clusters exist on a 

scale of the order of kilometers (Korolev et al. 2003). Such distributions are readily detected in 

the DR measurement with the GRIDS radar to identify the potentially hazardous liquid zones, 

and the radiometer will measure the liquid in either situation. However, rain rates sufficient to 

contaminate the radiometric LW measurement will complicate the detection. GRIDS’ first line 

of defense is to rely on the rain-shield window-blower system of the radiometers. Furthermore, 

the core algorithm is required to recognize periods of rain, and to automatically make a rain-no 

rain decision to flag the estimate of the icing warning level when rain is occurring. 

Further enhancements to the core algorithm, to make weighting adjustments to other 

parameters in the icing algorithm during substantial rain events, are also possible using 

additional GRIDS measurements, as discussed in the design section on algorithm 

implementation. 

2.3 Temperature Profiling 

In addition to identifying the presence and altitudes of liquid water through radar and 

radiometer measurements, GRIDS must determine whether the detected liquid is supercooled. 

Timely information about the local vertical profile of temperature is therefore required. 

Foremost, the system therefore must determine the altitude of the 0°C level, and the altitude of 
11

the clouds relative to that. Although the GRIDS radar makes continuous measurements of base 

and top altitudes of cloud layers, accurate means are required to estimate the temperatures within 

those layers. As enhancements to the core icing algorithm are developed, the full tropospheric 

temperature profile will be useful since most severe icing occurs between 0°C and -10°C, and 

thermodynamic instability determined from the profiles can indicate forcing to produce the 

condensation of liquid. 

The options for determining temperature include radar estimation of the height of the 

melting level, nearby radiosondes, output data from numerical weather prediction (NWP) 

models, and measurements from microwave radiometers operated at an additional frequency. 

Radiosonde data and NWP model outputs are both routinely and reliably available from NOAA 

Internet sites. 

2.3.1 Radar Detection of the Bright Band 

 Within clouds, the region where the temperature is approximately 0°C is where falling ice 

particles melt and become water droplets; this can be readily observed as the “bright band” with 

the GRIDS radar. Here the cloud reflectivity is enhanced, and the fall velocity, as measured in 

the Doppler signature, is accelerated as the snow is converted to drizzle or rain. However, the 

bright band, even when weak, is unmistakably and conveniently depicted in the DR signature, 

even when it is not evident in reflectivity, because the depolarization is enhanced by melting 

hydrometeors. Substantial gradients of 2-5 dB in DR have been measured between the edges and 

the core of the bright band, and the bright band has been observed to be 100m or more deep even 

when melting crystals are only producing drizzle (Reinking et al. 1996b). Thus, an algorithm 

that recognizes time-persistent gradients such as these along the fixed GRIDS radar beam will 

readily identify the bright band. Liquid water detected more than about 200 m above the height 

of the bright band is almost certainly supercooled. Unfortunately, bright bands can be 

ambiguous or not present at all, such as when the cloud is totally composed of liquid and there is 

no precipitation to melt, or the freezing level is at or just above ground level, or convective drafts 

mix melting through a deep volume. Thus, additional sources of local temperature information 

are required. 

2.3.2 Temperature Profiles from Radiosondes 

Radiosonde launches typically occur only twice per day, require 1–2 hours of flight time to 

profile the entire troposphere, and may be launched from sites 200–300 miles apart. 

Temperature fields aloft are less variable across large horizontal distances than surface 

temperatures or cloud fields. Thus, standard network radiosondes provide good reference data 

that are necessary for calibrating the GRIDS microwave radiometer, but the data are too coarse 

to be counted on to provide the timely and local temperature profile that GRIDS requires to 

determine cloud liquid cooling. Therefore, the GRIDS design allows ingest of the closest 

radiosonde data for calibrations, and these temperature data will be available as backup 

information. For GRIDS temperature profiling, the NWS network radiosonde data are best used 

as assimilated in the operational Rapid Update Cycle (RUC) numerical observation and forecast 

model. 
12

2.3.3 RUC Model 

The Rapid Update Cycle (RUC) atmospheric prediction system has been chosen as the 

primary source of temperature profile information for GRIDS, and allowance is made for checks 

of this value via the other options for determining temperature. The RUC is maintained by 

NOAA Forecast Systems Laboratory (FSL) and runs operationally at the National Center for 

Environmental Prediction (NCEP). 

 The RUC produces updates with the highest frequency of any forecast and assimilation 

model in the United States. This system is a combination of a data assimilation tool and a 

sophisticated mesoscale forecast model. A 20-km 50-level hourly version of it was implemented 

in 2002 (Benjamin et al. 2004b). The RUC uses a hybrid isentropic-sigma vertical coordinate, 

which improves accuracy for moist reversible processes. Thus, the moisture environment for 

areas of cloud development and precipitation may be forecast with better coherence. Also, 

isentropic coordinates are advantageous in that they “provide adaptive vertical resolution, greater 

in layers of higher static stability where strong vertical gradients of other variables are likely to 

occur” (Benjamin et al. 2004a). The RUC parameterizations include mixed-phase cloud 

microphysics. The combination of these features makes it the optimal model for obtaining 

temperature soundings in potential icing conditions. The regional analyses are based on data 

assimilated from multifarious sources including surface (land-based and ship-borne), upper air 

(balloon-borne radiosondes), and commercial aircraft flight-level (in situ) observations. The 

assimilation is performed via an optimal interpolation multivariate analysis procedure. Every 

hour, RUC issues updated meteorological analyses and forecasts, thus allowing extraction of 

site-specific temperature soundings with reasonable temporal resolution. GRIDS will extract 

these soundings to sites that are selected for deployment. 

2.3.4 Radiometric Temperature Profiling 

Microwave radiometry offers a means to provide temperature profiles continuously, at least 

in the lower troposphere. Radiometric temperature profiling can be accomplished by measuring 

the emissions through a band around 50–60GHz. Scanning through a narrow spectrum here will 

allow receipt of emissions from a range of distances into the atmosphere. The emission at any 

altitude is proportional to the temperature, so the profile can be obtained. However, microwave 

temperature profiling is most accurate in the atmospheric boundary layer, below about 2 km 

Above Ground Level (AGL). A comparison to radiosondes in the winter atmosphere shows that 

temperature retrieval accuracy is best near the surface and degrades upward to a standard 

deviation of 1.5°K at 3 km AGL. Also, the effective vertical resolution of the temperature 

measurement degrades linearly with increased altitude at 0.44z, where z is AGL height (Cimini 

et al. 2006). Thus, at 1 km it is ~440 m and at 3 km it is ~1,320 m. Temperature profiles to 

higher altitudes were measured by spiraling aircraft and a radiometer during the second Alliance 

Icing Research Study (AIRSII; Reehorst et al. 2005). The radiometer provided profiles to 10 km 

AGL, but the aircraft provided measurements only through cloud layers below 4 km AGL. 

Taking the aircraft measurements as “truth”, the radiometer estimates differed by as much as 3–

6°C in each of several test soundings, with these greater errors occurring within temperature 

inversions. Comparisons of radiosonde and radiometer data indicated temperature differences 

with standard deviations of 1.5–2°C (e.g., Chan and Tam 2005). 

Thus, the temperature profiling radiometer is limited in measuring detail, generally 

overestimating temperature in the upper reaches of inversions, and underestimating in the lower 
13

regions, but it can provide a good smoothed temperature profile that is continuously available 

with a temporal resolution of seconds, to verify and compare to the RUC output. This will be 

particularly important in situations with frontal activity where the 1-hour resolution and timeinterpolation of the RUC may become too coarse. These radiometric temperature data are not 

included in the GRIDS core algorithm, but development as an enhancement is recommended. 

As an interim development, it is possible to estimate the layer-mean temperature of cloud 

liquid using two channels: ~30 GHz and ~90 GHz (Schneider et al. 2004). This radiometric 

approach was first suggested by Koldaev et al. (1998). This approach was adopted and modified 

for use with GRIDS to provide the additional information in remote sensing of icing conditions. 

The exact microwave frequencies of the NASA-Glenn radiometric system used with this 

approach during AIRSII were 89 and 30 GHz. Optical thicknesses at these two frequencies 

obtained from measured brightness temperatures are the sum of the cloud optical thicknesses 

(τw) and optical thicknesses due to water vapor (τv) and oxygen (τo). After removing the 

gaseous components τv and τo using the value of the total integrated water vapor from standard 

radiometric retrievals and surface values of temperature and pressure, the time series of the ratio 

τw (89 GHz) / τw (30 GHz) is calculated. This ratio depends on the cloud liquid water 

temperature as shown in Figure 3, which shows that the condition 

τw(89 GHz) / τw(30 GHz) < 5.6 (or τw (30 GHz) / τw (89 GHz) > 0.18) corresponds to 

supercooled water temperatures. 
14

Figure 3. Ratios τw (89 GHz) / τw (30 GHz) (blue) and τw(30 GHz)/ τw(89 GHz) (red) as a 

function of temperature (from Schneider et al. 2004). 

Figure 4 shows results of estimation ratios in layers of “warm” (red) and “cold” (blue) liquid 

water stratus clouds, as a test of the technique during AIRSII. Average slopes of the best linear 

fit equations represent a mean value for the optical thickness ratio τw(89 GHz)/ τw(30 GHz) and 

thus can be used for estimating mean cloud liquid temperatures. These temperatures are about 

+7°C and -12°C for the “warm” and “cold” stratus cases, respectively. The fact that the best 

linear fit lines approximately cross the (0, 0) coordinate origin point indicates that the removal of 

the gaseous optical thicknesses τv and τo was generally accurate. This technique will be most 

helpful if the cloud liquid is entirely supercooled. However, when a cloud layer harbors both 

warm and supercooled liquid, a mean temperature warmer than 0°C can be deceiving, so care 

must be exercised to recognize that part of the cloud is supercooled and potentially hazardous. 
15

Figure 4. Illustrations of the mean liquid water temperature retrievals in “warm” and “cold” 

stratus during AIRSII (from Schneider et al. 2004). 

2.4 Icing Hazard Algorithm 

The several measurements and data ingests designed into GRIDS must be integrated in a 

meteorologically and technologically sound way to provide an output that will identify and warn 

of potential aircraft icing hazards. The icing algorithm brings together all the relevant 

measurements from GRIDS to make and issue a decision about the current icing hazard. GRIDS 

can address all-liquid clouds, mixed-phase clouds, and glaciated clouds. 

Some perspective on the expected values is helpful. Pilot report (PIREP) statistics (Schultz 

and Politovich 1992) and many other more recent studies with instrumented aircraft indicate that 

about 90% of icing events occur in clouds with temperatures between 0°C and -20°C, below 

6 km MSL, or within 9.3 km range for a radar elevation angle set at 40°, the fixed-beam selected 

for GRIDS. The required measurements are easily achieved through this short range with the 

GRIDS radar. The 50–500 μm droplets defined as supercooled large droplets (SLD) generally 
16

cause reflectivities between -15 and +5 dBZ, a range readily handled with a large margin with 

the GRIDS radar, and use of the Ka-band radar allows a good margin for detection of high 

concentrations of still smaller droplets (<50 μm diameter). 

Heggli and Rauber (1988) established a climatology of LW in mountain-forced winter 

clouds, including those events driven by storms. They found that the column-integrated LW was 

less than 0.2 mm 85% of the time, but storm periods produced many hours in the 0.5 – 1.0 mm 

range. This range is generally supported by other studies. Some values reaching as high as 

2.0 mm can be expected; some column liquid values of this order were measured under wave 

forcing (Reinking et al. 2000a) and during the second Alliance Icing Research Project (AIRSII) 

with ETL and NASA radiometers in 2003. 

The core icing algorithm uses four decision points based on the slant-path, fixed beam 

measurements of liquid water (LW), radar reflectivity Ze, radar depolarization ratio (DR), and on 

the ingested temperature profile. The following parameters and values have been incorporated 

into the GRIDS core icing algorithm. A hazardous region within a cloud is identified as one that 

exhibits a temperature low enough to cause droplet supercooling (T < 0°C), a reflectivity large 

enough to warrant consideration (Ze > -23 dBZ), and a DR that matches the spherical 

hydrometeor signature (± 2 dB), all while the radiometer indicates significant liquid water along 

the path (LW > 0.05 mm—the minimum detection threshold). A reflectivity Ze of -23 dBZ 

approximates that point where the minimum threshold for moderate icing conditions is met at the 

smallest effective droplet diameter, De, considered in the Federal Aviation Regulations (FAR) 

Appendix C icing envelopes. That point is (LWC, De, T) = (0.3 g m-3, 15 μm, -20o C). The 

lower-threshold value of the depolarization ratio, DRth, depends on the properties of the antenna; 

it is a few decibels more than the antenna cross-talk, which is nominally specified for -30 dB. 

The GRIDS core icing algorithm, as derived from these decision points is summarized in Figure 

5. The icing potential is indicated in a continuous time-altitude display (as schematically 

depicted in Figure 1) updated every minute, such that the real-time conditions and a short history 

(e.g., several hours) are presented as an evolving profile through all clouds in the radar beam. 

Thus, the core icing algorithm uses four decision points based on the slant-path, fixed beam 

measurements of liquid water (LW), radar reflectivity Ze, radar depolarization ratio (DR), and on 

the ingested temperature profile. 
17

Figure 5. The decision points for the GRIDS core icing hazard (from Schneider and 

Campbell 2006). 

The GRIDS algorithm is a vital, evolving decision-making tool. This perspective for the 

GRIDS core algorithm must be recognized. The core algorithm is an effective beginning, with 

much potential for enhancements. Just as algorithms for the NEXRAD have been continuously 

improved since that system was made operational, the GRIDS algorithm can gain power with 

continuing upgrades. Further, GRIDS or any other measurement or modeling system can only 

warn of a potential hazard. An accident-causing hazard is dependent on the aircraft and its 

deicing capabilities as well as the cloud environment. An aircraft may collect too much ice very 

rapidly in a high liquid, SLD cloud, or by flying too long, e.g., in a holding pattern, in a lowliquid small-droplet cloud. GRIDS will detect both and warn of the general hazard, but cannot 

include characteristics of individual types of aircraft. 

The specifics of the implementation of this algorithm, including the core algorithm and 

enhancements thereto, are outlined in Section 3.3 and discussed in more detail in Appendix B. 

2.5 GRIDS Operational Experience 

The GRIDS design described in this document is based on tests with ETL’s scanning Kaband radar (NOAA/K), a fixed-beam Ka-band radar (MMCR), and on component design and 

construction selected specifically for an operational GRIDS. NOAA/K was operated in the 

series of WISP and related experiments and has provided the proof of concept. Thus, the 

fundamental icing-detection algorithm was derived from the evolving polarization theory and 

field testing which has been extensively referenced in this document. ETL’s Millimeter-wave 

Cloud Radar (MMCR), a vertically pointing, fixed-beam system that is in continuous operational 

use at the several DOE Atmospheric Radiation Monitoring (ARM) sites, has provided a 
18

hardware prototype for the operational GRIDS. Tests of the algorithm were conducted by 

operating NOAA/K in the same mode as planned for GRIDS in order to validate the concepts 

that integrate the data from the separate systems and inputs. Two experiments were performed, 

AIRS II and WISP04, and both incorporated a radiometer measuring LW and WV and the RUC 

model. The icing algorithm was calculated in real time and data were sent via the Internet to 

ETL, where it was archived and displayed. The temperature profile was taken from the RUC 

model. 

Analyses of two field tests, one from AIRS II and one from WISP04, as analyzed by 

Schneider et al. (2005) and Bernstein and Schneider (2004), respectively, demonstrate the 

functionality of GRIDS. 

2.5.1 AIRS II—Essence of Case of 11 November 2003 

Meteorologically, deep, glaciated, snowing clouds early in the period contributed minimal 

icing threats over Mirabel Airport, Montreal, the site of the AIRS II experiments. Later, midlevel drying occurred, leaving lower, water-dominated clouds with warmer (but still sub-zero) 

tops that were being lifted over a warm frontal surface. These clouds carried significant water 

contents and SLD, which resulted in moderate to severe icing conditions. GRIDS documented 

the fine structure of clouds throughout the period and recorded signatures of the SLD, the 

presence and timing of which were verified by three research aircraft in coordinated flight. The 

aircraft samples reported here are mainly from a NASA Twin Otter aircraft. 

Early in the period, the aircraft found cloud above the melting level to be dominated by 

snow, with most liquid well consumed by the crystal growth processes (Figure 6). From vertical 

profiles prior to ~2000 UTC, the various aircraft sampled low liquid water contents (LWC), in 

the range of 0.1 to 0.25 g m-3. 

However, samples with one aircraft after ~2009 UTC found rapid change resulting in LWC 

of 0.15 – 0.5 g m-3, and freezing drizzle around -2°C between 1.7 and 2.3 km AGL (top of the 

profile) at ~2018 UTC, and recorded 100–300-micron-diameter SLD (Figure 7). The pilots 

reported ice accretions aft of the ice protection system in moderate-to-severe conditions, while 

other aircraft found icing up to 3.7 km AGL. Cloud tops continued to descend and eventually 

became too warm for icing, but the drizzle formation process continued, as documented by the 

Twin Otter aircraft. 
19

Figure 6. Individual ice crystal images observed by the NASA Twin Otter 2DC-Grey probe at 

1635 UTC. 

Figure 7. Individual freezing drizzle droplet (SLD) images observed by the NASA Twin Otter 

2DC-Grey probe at 2031 UTC. 

At ~1900 UTC, the RUC model and the aircraft, in reasonable agreement, placed the 

melting level at ~1.5 km. The RUC held the melting level constant throughout the period of 

interest, 1900–1945 UTC. During AIRS II, and including this case, the GRIDS radar was 

operated nominally on a 20-minute cycle, with 18 minutes staring at 40° elevation with a fixed 

azimuth, followed by two orthogonal RHI (vertical-slice) scans to provide spatial context and 

information on the angular dependence of DR. The GRIDS icing hazard product for the period 

of interest is shown in Figure 8. 
20

The extensive yellow areas denote regions where caution was advised within the changing 

boundaries of the clouds; during these periods the GRIDS microwave radiometer confirmed 

supercooled liquid above the algorithm threshold, but due to the mixed phase, the radar did not 

determine where within the cloud the liquid actually resided. The depolarization measurements 

from the radar, combined with the radiometer, did indicate dangerous SLD (DR < ~ -28 dB) at 

times and altitudes indicated in red (Figure 8). Included here are red conditions that began at 

2017 UTC, between 1.5 and 3.5 km AGL. This warning, which persisted through 2045 UTC, 

was consistent with data from the Twin Otter aircraft, which detected significant SLD on climbout at 2009 UTC and during its 2015–2031 period of level flight at ~2.3 km, as reported in 

Figure 7. This is in situ confirmation of the GRIDS algorithm. 

Figure 8. Time-height image of the GRIDS icing hazard. Green=safe; yellow=caution; 

red=definitive hazard. The blue horizontal line at 1.5 km altitude indicates the freezing level 

determined from the RUC model. The vertical grey bars are periods where the radar was 

scanning or pointing vertically. 

The location of predominant LWC within the vertical column, even though not specifically 

identified in the DR signature, is potentially measurable using vertical Doppler spectra. Spectral 

information would be an enhancement to the core GRIDS algorithm. When any of the aircraft 

were sampling in vertical profiles over the Mirabel Airport, the GRIDS radar was sometimes 

operated in a vertically pointing time series mode, in order to measure the spectra of cloud 

particle fall speeds. An exemplary set of measurements was taken between 2007 and 2011 UTC 

during this case study (Figure 8 shows a vertical grey bar for this period). In Figure 9, fivesecond time-averaged spectra that were sampled three times in this 4-minute period are presented 

at selected altitudes through the cloud layers. 
21

Figure 9. Spectra of vertical velocity as a function of altitude from 4-min time series 

measurements with GRIDS, 2007–2011 UTC. The minimum value in each spectrum indicates 

the corresponding altitude. 

The Twin Otter aircraft generally encountered an ice-dominated environment during a 

descent just prior to 2009 UTC and a liquid-dominated environment during its foray after 2009 

UTC. The GRIDS radar spectra were centered on this time. The spectra above 4 km have 

narrow peaks and a downward motion of ~0.5 ms-1; these are characteristic of settling ice 

crystals. The aircraft found ice crystals and minimal LWC above 3.5 km. Between ~2.8 and 3.3 

km altitude, the spectra vary considerably and are mostly characterized by their breadth and/or 
22

bimodality straddling the 0 ms-1 boundary separating upward from downward motion. These 

features suggest the generation of liquid, i.e., small droplets with negligible fall speeds carried 

with cloud motion, in the presence of settling ice. Some 14 min earlier, the aircraft found 

crystals and their aggregates here, but essentially no liquid. The time difference is significant. 

Supercooled liquid water (SLW) became evident again in the aircraft samples below 2.5 km at 

1953 UTC in mixed-phase clouds. The spectra show downward increases in hydrometeor fall 

speeds below this level, indicating growing ice crystals. However, bimodalities again appear 

between 1 and 2 km, corresponding with the aircraft detection of drizzle-sized droplets (SLD). 

This case is presented in more detail by Schneider et al. (2005). More about the addition of 

Doppler spectral measurements to enhance the GRIDS algorithm is discussed in Appendix B. 

2.5.2 WISP04—Essence of Case of 10–11 March 

The following test case illustrates not only the performance of GRIDS but the synergy with 

the Current Icing Potential Model (CIP; Bernstein et al. 2004), which was also developed under 

the FAA’s Aviation Weather Research Program over the same years as ETL’s GRIDS. Since 

each integrates different sources of icing-related data to indicate the presence of supercooled 

water drops, both at small and large sizes, they can provide largely independent and 

complementary assessments of the hazard and thus add (or subtract) confidence in the combined 

result. During WISP04, GRIDS operated out of its base at Erie, CO, while CIP was running in 

real time at NCAR. This test case is taken from WISP04, as analyzed by Bernstein and 

Schneider (2004). Additional such studies can be found in the analyses of Schneider et al. 

(2004) and Schneider and Campbell (2006). 

This icing event, which occurred on 10–11 March 2004, manifested itself as post-frontal, 

upslope stratus with some embedded showers, and impacted the plains of northeastern Colorado. 

Both GRIDS and CIP diagnosed the presence of icing and provided unique perspectives on the 

event. These diagnoses were verified by comparison to aircraft observations of in-cloud “truth” 

from the North Dakota Citation. 

GRIDS measurements are nominally averaged to provide 1-min resolution data (higher 

temporal resolutions are possible). The spatial resolution of GRIDS is nominally 37.5 m. CIP 

uses GOES satellite data and surface observations to find clouds and identify their bases, tops, 

and cloud top temperatures. It combines this information with a mosaic of NEXRAD radar 

reflectivity, surface observations of precipitation, observations of lightning from the national 

lightning detection network, recent pilot reports, and 3-hr RUC model forecasts of temperature, 

relative humidity, vertical velocity, and supercooled-liquid water to estimate the potential for 

icing and SLD conditions across the contiguous United States and southern Canada. 

During WISP04, an operational, 40-km-resolution version of CIP ran at NOAA’s Aviation 

Weather Center (AWC) and a then-experimental, 20-km version ran at NCAR. The 20-km 

version, used here, contains many important upgrades and was implemented in 2005 at AWC. 

The University of North Dakota’s aircraft was flown to document the microphysical 

characteristics of clouds and precipitation over the WISP04 domain. The Citation carries an 

array of probes to record state parameters and cloud physics fields. 

Early in the test period for this case, little or no supercooled liquid water was present in the 

clouds, as evidenced in the intermittent, low integrated water contents measured by the GRIDS 

radiometer through ~2000 UTC (Figure 10). Following a secondary frontal passage, 
23

temperatures dropped rapidly toward freezing and low stratus clouds moved into the WISP04 

domain. These clouds contained significant amounts of supercooled liquid water which 

impacted the area between 2100 and 0400 UTC (Figure 10), resulting in numerous reports of 

light and moderate icing. Water contents dropped to near zero by 0400, although skies did not 

clear immediately. 

Figure 10. Integrated vapor and liquid values measured by the GRIDS radiometer for the period 

1200 UTC, 10 March through 1200 UTC, 11 March. 

In accord with the GRIDS radiometer, the UND aircraft measured LWC during climb out at 

~2230 UTC (Figure 11a), and during a subsequent series of flight legs at stepped altitudes 

(Figure 11b) ending at 0001 UTC. Cloud base and top were found near 2.3 and 3.6 km (all 

heights MSL). Cloud top and base temperatures were -7.7o

C and -2.5oC, respectively, which 

were ideal for icing conditions. Measurable LWC was found throughout this cloud layer, with 

the most significant concentrations consistently near 0.35-0.55 g m-3 and peak values to 0.8 g m-3

in the upper part of the cloud. During a brief second flight (not shown), uniform icing conditions 

with LWC of 0.5 g m-3 were found near 3.0 km MSL between 0100 and 0200 UTC. 
24

1500

2000

2500

3000

3500

4000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7

LWC (gm-3)

Pressure Altitude (m MSL)

"FSSP"

"King"

Figure 11a. Liquid water content measured during climb out at ~2230 UTC. Altitudes are in 

meters above MSL. Magenta and blue lines trace water contents measured by two different 

probes. 

Figure 11b. Liquid water content measured during climb out at ~2230 UTC in a distance-height 

cross section. Color scale shows water content in g m-3. Altitudes are in meters above MSL. 

The horizontal axis is in degrees of latitude. 
25

The GRIDS Ka-band radar depiction of the clouds during this case showed relatively 

dynamic clouds for the first half of the event, then weaker, more stratiform clouds during the 

second half. Depolarization ratio measurements indicated that cloud ice was present somewhere 

in the column throughout the duration of this case. Prior to 2100 UTC, the Ka-band radar 

indicated that there were ice clouds between 2 and 5 km MSL (Figure 12). By 1915 UTC there 

was a bright band which persisted until 2050 UTC, showing the melting level to be just over 

2 km MSL during this period. The GRIDS icing algorithm (Figure 13) issued icing cautions 

(yellow) throughout most of the duration of the case until the system dissipated, including and in 

accord with the in situ truth of SLW measured during the 2230–0000 flight of the UND aircraft. 

The radar was documenting the presence of ice and the radiometer was documenting the 

presence of liquid, thus confirming mixed-phase clouds observed by the aircraft. The yellow 

warning was intermittent prior to ~2045 UTC, reflecting the intermittent LW measured with the 

radiometer (Figure 10). Interspersed were three zones with red warnings of icing, including very 

two slight ones at ~1945, at ~0200 UTC, and a very significant one between 2230 and 2300 UTC 

below ~3.5 km MSL. Notably, the algorithm appropriately flagged the main “condition red” as 

the aircraft confirmed the significant SLW during and following climb out (~22:30 UTC, Figure 

11a). 

Figure 12. Vertical slice (RHI scan) of the depolarization ratio (DR) at 2002 UTC, parallel to an 

aircraft transect. The DR clearly shows the bright band at ~2 km MSL (~0.5 km AGL). DR > 

-32 dB indicates the presence of ice. Range is relative to radar. 

During the 1915–2050 UTC period, when the radar documented the bright band at 0.8 km 

AGL or 2300 m MSL, the RUC model had the freezing level at 2800 m MSL, nearly 500 m 

higher (Figure 13). The GRIDS algorithm most likely missed lower-level icing hazards because 

of the RUC model error, as determined by the radar bright-band truth. The incorporation of the 
26

bright-band altitude enhancement to the algorithm (as outlined in Appendix B) and use of the 

additional, 55-GHz radiometer channel to determine low-altitude cloud temperatures (as in 

Section 2.3.4 and Appendix B) to serve as cross-checks on the RUC, will certainly increase the 

accuracy of the GRIDS algorithm. These can be readily implemented. 

Figure 13. Time-height (MSL) image of the GRIDS icing product (green=safe; yellow=caution; 

red=serious hazard), with a cloud boundary overlay (black contour) and the zero degree isotherm 

(blue line) from the hourly RUC product. 

The CIP model diagnosed icing conditions across northeastern Colorado throughout the 

period of the flights. At approximately the time of the aircraft climb out, CIP indicated potential 

for icing between 2.5 and 4.3 km, with high potentials (0.8–1.0 on a scale of 0.0–1.0) between 

2.8 and 3.7 km (Figure 14). This range of altitudes very closely matches the core icing altitudes 

observed by the aircraft and GRIDS, both during its climb and during the horizontal flight legs, 

but generally overestimated the total depth of the icing layer (see Figure 11a and Figure 13). 

Using GOES observations of clouds with -9oC cloud-top temperatures in combination with the 

RUC profile of temperature (compared to the -7.7oC value observed by the aircraft), CIP 

overestimated the cloud-top height by ~0.5 km (~1500 ft). The overestimate was caused by a 

combination of slightly cold satellite-measured cloud-top temperature (CTT), slightly warm 

RUC forecast temperatures which also affect the GRIDS algorithm, and CIP’s conservative 

method of cloud-top height estimation, which places the cloud top at the first vertical level that is 

colder than the satellite-observed cloud-top temperature. 

Local ceiling observations indicated a cloud base of 2.6 km MSL, which CIP brought down 

to the next lower grid point, vertically, at 2.5 km. Ideal icing temperatures (-3o

C to -9oC), 

satellite-indicated cloud top at -9oC, plus both high relative humidity (>90%) and weak upward 

motion from the RUC all contributed to the high icing potentials. Although the cloud top height 

was overestimated by 0.5 km, low relative humidity values and slight downward motion at 

altitudes between the actual and the CIP-diagnosed cloud tops resulted in low (0.1-0.4) icing 
27

potentials there. Warm RUC temperatures also resulted in low icing potentials (0.35) near cloud 

base. 

CIP diagnosed icing all along the flight track, and continued it southwestward into the 

Colorado foothills and northeastward to the Nebraska border (Figure 14). Icing was consistently 

depicted during the hours of flight, when icing was encountered aloft and the GRIDS radiometer 

showed significant amounts of integrated liquid water (Figure 10). 

Figure 14. Horizontal and vertical cross sections of CIP icing potential for 2300 UTC a) at 

~3.0 km (700 mb) altitude and b) vertically, along the black line in part (a). White crosses in 

part (a) indicate the locations of the aircraft flight legs. Brown filled areas indicate topography. 

In sum, both GRIDS and CIP were able to diagnose the icing potential that occurred on this 

day. As with all systems, the GRIDS and CIP methods each have their strengths and weaknesses 

affecting diagnoses for different icing events. While CIP consistently depicted icing throughout 

the ~7 hour event, it provided relatively coarse, hourly output with 20 km horizontal and 0.3 km 

(1000 ft) vertical grid spacing. GRIDS demonstrated the ability to diagnose the icing on very 

fine time (1 min) and space (0.1 km) scales, accurately portraying the regions which contained 

icing, as would be appropriate in the immediate airspace of an airport. 

GRIDS can accurately identify the altitudes of clouds and provide estimates of their phase 

and liquid water content. CIP’s satellite- and surface-observation based cloud scheme can help 

to fill the gaps in GRIDS’ cloud field when radar signals are weak or when heavy precipitation 

saturates the Ka-band signal, and to extend GRIDS’ point-like measurements over a broader 

region. These two systems can complement each other particularly well in a terminal-scale 

setting, especially when development of the new terminal-scale version of CIP, running at 5 km 

and 15 min resolution, is completed. In all, GRIDS and CIP operated simultaneously would 

enhance the total icing product. 

Part a Part b 
28

The field tests from these case studies further validate the fundamentals of the algorithm and 

GRIDS. Note that the GRIDS design calls for a much more sensitive Ka-band radar (work in 

progress) than was used during WISP04, which will improve the hydrometeor discrimination and 

extend the range of application of GRIDS. Full construction and operational implementation of 

GRIDS on a prototype basis is the next step, and is left to the discretion of potential users. 

3. DESIGN OF GRIDS 

General requirements for GRIDS are given in Appendix C, as well as a table of operating 

modes. In the following sections we divide the overall GRIDS design into its major components 

and discuss each individually. Below, in Figure 15, is a simplified block diagram that shows 

how the various sub-systems of GRIDS interconnect. For a more comprehensive block diagram 

of GRIDS, see Appendix D. Appendix E gives a list of GRIDS Features and Benefits. 
29

Figure 15. Simplified block diagram of GRIDS showing major sub-systems. For a detailed 

block diagram, see Appendix D. DIFRS, RC+, and RIPS are each Linux-based computers. 

3.1 Sensors 

Research leading to GRIDS indicates that the icing hazard within a cloud can be ascertained 

by combining three measurements: 1) depolarization of microwave energy scattered from cloud 

particles, 2) liquid water content along the path containing the scattering particles, and 3) the 

temperature profile within the cloud. These three measurements can be made using currently 

available technology, and are discussed in the following sections. 

Container 

Internet 

Received Signal

Radar Timing

LAN 

Monitoring & System Control 

1 Gbit 

Ethernet

Radar 

Auxiliary 

Computer 

RC+ 

Radar Control 

Plus 

DIFRS 

Digital IF 

Receiver 

Subsystem 

Radiometer 

Computer 

Firewall 

Computer 

Temperature

Humidity 

Pressure 

Precipitation 

(surface 

sensors) 

RUC Model 

Data 

Icing Display 

and Archival 

Data 

RIPS 

Remote 

Integrated 

Process Server 

Radiometer 
30

3.1.1 Radar 

Our design of a radar suitable for operational use is based upon an award-winning design by 

ETL (Moran et al. 1998) that has proven its power, flexibility, and robustness with nine years of 

continuous operation at a number of remote Department of Energy (DOE) climate-observing 

field sites worldwide. It will be discussed here only in a cursory manner since its design is well 

documented in the Moran paper. The final transmitter stage in DOE’s Milli-Meter Cloud Radar 

(MMCR) systems, and in GRIDS, is a low peak power, but high average power Traveling Wave 

Tube Amplifier (TWTA), designed originally for use in satellites. Average power is increased in 

one of two ways (or both) with respect to more conventional final transmitter stages such as 

magnetrons: high pulse repetition frequency (PRF), and/or transmitting pulses of long duration 

with embedded pulse coding (to maintain range resolution), which is known as pulse 

compression. For GRIDS, no pulse coding will be used to increase power (and hence sensitivity) 

because of the complicating range-velocity sidelobes that can result, and because adequate 

sensitivity can be obtained by other means. However, the design will accommodate the addition 

of pulse compression in the future if it is determined that more sensitivity is required. 

GRIDS will have a TWTA with a peak transmitted power of 1000 watts. TWTAs are 

designed to operate unattended for long periods of time, with the manufacturer stating an 

expected lifetime of 2.3 years at continuous operation. However, experience with DOE radars 

indicates that the actual lifetime of these devices is considerably longer. 

All of the radar components are designed to function in a standard laboratory environment 

(76 degrees F, low relative humidity). The radar is therefore housed in two racks inside an 

environmentally controlled transportable container, with the antenna mounted outside but as 

close as possible to the transmitter. The antenna will point the radar beam at an elevation angle 

of about 40o above the horizon, and (optionally) in the zenith direction (elevation angle of 90o). 

The radar will have an adjustable pulse width with 1.55 and 1.00 μs pulses planned for first 

use, giving range resolutions of 232 and 150 meters, respectively. It is planned to use the 1.55 μs 

pulse width for the 40o elevation angle, and the 1.00 μs pulse width for possible zenith operation, 

giving a height resolution in both cases of 150 m. Thus, sensitivity at a given height above 

ground and height resolution will be approximately equal for the two elevation angles. 

The desired minimum detectable signal at a height of 5 km above the radar is -140 dBm for 

GRIDS, which corresponds to an equivalent radar reflectivity factor (Ze) of -65 dBZ. 

(Henceforth, we will refer to Ze simply as reflectivity.) The present GRIDS design allows the 

antenna elevation angle to be manually adjusted between 30o and 90o. It would be desirable for 

GRIDS to have the capability for automatic angle changes commanded by computer to one of 

two positions (most likely 40o and 90o). For this capability, simple motors and stops would be 

added, at some added cost. 

The microwave portion of the radar is shown in Figure 16 in block diagram form. The 

transmit pulse originates as a digital pulse in the Radar Timing Generator (not shown). From it, 

a 60 MHz pulsed IF signal is generated in the IF Modulator and sent to the Coherent Up/Down 

Converter (CUD). The CUD contains a solid state source whose output is used to synthesize all 

the frequencies needed to up-convert the transmitted signal and down-convert the received 

signals. The pulsed IF signal is then converted to RF (34.8 GHz) and then amplified by the 

TWTA to a 1000-watt pulse. The pulse is then sent through the Circulator Assembly, which 

functions mainly as a transmit/receive switch. The Circulator Assembly also serves to switch a 
31

noise source into the receiver channel during calibration. The pulse then passes through the 

Ortho Mode Transducer (OMT), which transitions it to the circular waveguide, and then to the 

polarizer, which converts the linearly polarized wave to circular polarization. This signal is then 

radiated by the antenna as a circularly polarized wave. 

Figure 16. Diagram of radar in GRIDS showing the major components. The Circulator 

Assembly functions as a transmit/receive switch and also allows for injection of a noise source 

for receiver calibration. 

When the radiated pulse strikes a scatterer, it is reflected back to the antenna. Specular 

(mirror-like) reflection will cause a polarization reversal (in circularly polarized waves) of the 

backscattered signal. So, for instance, if right-hand circular polarization (RHCP) were 

transmitted, then left-hand circular polarization (LHCP) would be received at the antenna from a 

non-depolarizing target. The received energy passes through the polarizer which converts it to 

linear polarization, and then into the OMT which separates the energy out into horizontally and 

vertically polarized waves at the two linear ports. These two ports are designated Main and 

Cross, with the Main port having the generally stronger signal because it is the cross-polarized 

port from the transmitted signal. 

The two receive channels now enter the Circulator Assembly, pass through transmit/receive 

switching and are routed to the low-noise amplifiers (LNA), where they are amplified and passed 

to the Coherent Up/Down Converter (CUDC). The CUDC mixes the signals down to the 60 

XMIT RF

Cross RF 

Main RF 

XMIT IF 

Cross RF Main RF

LNAs Cross IF 

Main IF 

IF Modulator/ 

Receiver 

Coherent 

Up/Down 

Converter 

TWTA 

+50 dB gain

Circulator 

Assembly 

XMIT 

Pulse 

Main & Cross 

IF 

circular 

waveguide

Pol 

OMT 
32

MHz IF frequencies, which are passed to the IF Modulator/Receiver where they are amplified to 

a suitable level to drive the A/D converters in the digital receiver (not shown). 

Table 2 shows the characteristics of the radar portion of GRIDS when operating at 40° 

elevation and with 64-point Fast Fourier Transforms (FFTs). For characteristics in other modes, 

refer to Appendix C. 

Table 2. Radar characteristics of GRIDS operating at 40° elevation 

Physical characteristics elevation angle 40 deg 

 radar wavelength 8.6 mm 

 antenna diameter 3.0 m 

 peak transmitter power 1000 watts 

 elevation angle 40.2 degrees 

 transmitter path loss 2 dB 

 receiver noise figure 5.5 dB 

Parameters alterable by 

software 

pulse repetition period 110 μs 

 pulse width 1.55 μs 

 number of FFT points 64 points 

 dwell time 60 s 

 range gate spacing 1.55 μs 

 number of range gates 69 

 receiver bandwidth 0.645 MHz 

Dependent characteristics unambiguous radar range 16.49 km 

 minimum height 68 m 

 maximum usable height 10.2 km 

 maximum unambiguous radial velocity ±19.55 m/s 

 maximum unambiguous horizontal velocity ±25.60 m/s 

 time available for one spectrum computation 51 μs 

 number of spectrum averaged per gate per 

dwell time 

8523 

 duty cycle 1.41% 

 average power 14.1 watts 

 range resolution 232.3 meters

 height resolution 150.0 meters

 radial velocity resolution 0.611 m/s 

 Estimated sensitivity at 5 km AGL using 

spectral processing (0.2 m/s spectral width) 

-69.4 dBZe 

 Estimated sensitivity at 10 km AGL using 

spectral processing (0.2 m/s spectral width) 

-63.4 dBZe 
33

3.1.1.1 Calibration Checks 

 One planned difference between the GRIDS radar and DOE’s MMCRs is the manner in 

which calibration checks will be performed. For MMCRs, about once every 10 days, a lengthy 

process is undertaken whereby radar operations are halted and many different levels of white 

noise are injected into the receiving channel, a transfer curve is produced (signal in vs. detected 

power) for the entire dynamic range of the receiver, and the radar constant is re-derived and 

compared to the default value. All measurements are saved. Only if the new radar constant 

differs markedly from the default value is action taken. For GRIDS we will perform calibration 

checks much more frequently, at least once per day. Instead of regenerating the entire transfer 

curve, however, we will check only two points on it – one point near the center of the receiver 

dynamic range and one point at the bottom (no signal in). 

The receiver design will allow this calibration to be done automatically, and it will be 

controlled and scheduled by the GRIDS software. Action will be taken only if the transfer 

values differ markedly from the current “correct” values. If the expected values from the 

calibration are off by only a small amount, then the calibration constants will be changed 

automatically. Large errors would indicate a severe problem and require service by a 

maintenance technician. Several years’ experience with five MMCRs has led us to take this 

simpler approach. The more thorough and time-consuming procedure has no additional practical 

benefit. Either procedure checks only the receiver’s calibration; it does not check calibration of 

components “outside” the receiver, such as the waveguide between the antenna and the radar’s 

electronics, and the antenna itself. These components typically remain stable unless damaged. 

The other variable is the transmitted power, which will be monitored automatically. 

3.1.1.2 Radome 

Radomes are antenna covers designed to protect the antenna from water buildup 

(snow/slush) and from birds and dust/dirt which are often a problem in unattended operation. 

The radome introduces a small loss by attenuating the radar signal on both transmission and 

reception (about 1 dB, two-way). This loss is normally accounted for in the antenna calibration, 

but additional loss will occur if the radome surface is coated with water or wet snow, perhaps 

during and after local events of precipitation. The amount of loss depends on the amount and 

thickness of water on the surface. For a vertically pointing antenna the radome surface is nearly 

horizontal. Often such radomes are designed with a 5o tilt, to allow water to run off. Still, 

attenuation values range from a few dB to 10 dB, for radome surface wetness ranging from a thin 

film of water to puddles of water. For wet snow, several inches can add more than 20 dB of 

attenuation to the radar signal. 

For unattended operation, the GRIDS’ antenna must have a radome to protect the surface of 

the dish from weather, birds, insects, etc. Since the antenna will be tilted at an elevation angle of 

40o either permanently or cyclically with 90o, the likelihood of water/snow buildup is reduced. 

Experience shows that dry snow will be shed from the radome with negligible impact. The 

radome may, however, still have a water film buildup during and after wet precipitation that can 

introduce up to 10 dB additional attenuation (two-way). The sensitivity of the radar will be 

reduced by this loss. The water film loss will not affect the depolarization ratio (DR) 

measurements since the loss is the same for both polarizations. Since the water film will affect 

the reflectivity measurement, calculated reflectivity will be lowered by the amount of this loss. 

But this can be mitigated somewhat by using the radar data itself and data from a rain gauge to 
34

estimate and correct for the additional loss. The data will also be flagged to indicate that the 

accuracy of the reflectivity is suspect. 

3.1.1.3 Receiver Channels 

GRIDS will use two independent receiver channels, each dedicated to one of the 

polarization states. This increases the sensitivity of each polarization channel by 3 dB, 

equivalent to doubling the transmitter power, as compared to having one receiver channel that is 

multiplexed between the two polarizations. The output of the two receiver channels is at IF 

frequencies, which is then digitized by a digital receiver card. 

3.1.2 Radiometers 

The GRIDS icing algorithm (Section 3.3) requires a measurement of integrated liquid water 

along the radar beam, as well as a frequent if not continuous temperature profile. These needs 

can be met by radiometers operating at both 20–30 GHz and around 55 GHz, although the 

55-GHz temperature profiling has limitations as well as the advantage of continuous monitoring, 

as detailed earlier. Through core algorithm upgrades, the 55 GHz temperature profiler readings 

can be integrated with RUC model temperatures and bright-band recognition to achieve a best 

estimate. Frequencies around 20–30 GHz yield integrated liquid water and water vapor 

measurements. Although water vapor is not required by GRIDS, it is available as a by-product 

of the liquid water measurement. Frequencies around 55 GHz are used to provide a temperature 

profile. 

A single enclosure housing all the radiometer equipment will be mounted on the roof of the 

GRIDS container. The radiometer system will be controlled by a PC inside the GRIDS 

container. 

Radiometers are available from ESRL/PSD, which, operating as ETL, developed the 

technology in the late 1980s and continues to improve microwave radiometry, or from 

commercial sources such as Radiometrics, Inc. (http://www.radiometrics.com/) whose model 

TP/WVP-3000 has the needed capabilities. 

3.1.3 Temperature profiles 

The RUC model analysis produces a self-consistent statement of the current state of the 

atmosphere. The operational version used in GRIDS algorithm testing (Section 2.5), known as 

RUC-20, produced updated three-dimensional analyses and forecasts every hour, covering the 

lower 48 states at 20-km horizontal resolution through 50 vertical layers. 

The current operational version, known as RUC-13, has 13-km horizontal resolution through 

50 vertical layers. This version of RUC has improved numerics in the assimilation package, as 

well as improved physics in the mesoscale model. RUC is scheduled to be improved again by 

2008 with an enhancement called Rapid Refresh. Real-time access to RUC data is available 

from NCEP (http://ruc.fsl.noaa.gov/). These data will be ingested into the GRIDS system via an 

Internet connection at hourly intervals. 

Temperature profiles from the network rawinsondes at 00Z and 12Z are assimilated into the 

RUC, so the RUC is most accurate near the times and locations of the sonde launches—normally 

first-order stations at or near airports, where GRIDS units also will be most appropriately 

located. Temperature profile and melting level data from any radar signatures of the bright band 
35

when available, and the 55 GHz radiometer will be compared to produce an algorithm for the 

most accurate profile possible. In this way, a temperature decision protocol will be established. 

3.1.4 Surface Meteorological Sensors 

Commercial off-the-shelf sensors (COTS) are used to furnish GRIDS with surface 

meteorological data consisting of temperature, relative humidity, atmospheric pressure, and rain 

rate. These data are used for station maintenance, radiometer calibration, diagnostic purposes, 

and data-quality assurance. 

3.2 On-Site Computer Systems 

There are six computers in the GRIDS container, all connected by a Local Area Network 

(LAN). This section describes both the hardware and software architecture of these computers. 

Below we describe the design for the GRIDS system of computers, what they do, how they 

do it, and how they communicate. We believe this design will result in a robust unattended 

instrument at minimum cost and effort. 

3.2.1 GRIDS Software Architecture 

The GRIDS Radar Acquisition and Display System (GRADS) is the heart of the GRIDS 

real-time data-processing system. It is named GRADS, as a combination of GRIDS and RADS. 

RADS is a computer system that has been used reliably on the ETL scanning radars for the past 

ten years. It is described by Campbell and Gibson (1997) and by Gibson et al. (2004). The 

GRADS is a multi-level distributed software architecture that has been custom-designed for the 

GRIDS. Its design allows for significant changes in the hardware and operating system. It 

consists of several distributed processes that allow for acquisition of four separate data streams, 

data integration, archival, and display. The system produces data files as well as graphical 

products in real time and has a Web server component as well. 

The top level GRADS is made up of two levels of distributed components, as shown in 

Figure 17. The next level is made up of the eXtended RADS or XRADS and the Remote 

Integrated Product Server, RIPS. XRADS acquires, processes, displays, and writes radar data 

and controls the radar. It is also composed of three distributed modules. The first is the Digital 

IF Receiver Subsystem or DIFRS. It is this part of the system that queries the Digital IF 

Receiver, services interrupts and acquires the data. It then sockets the data to the second module, 

Radar Control Plus, known as RC+. RC+ not only reformats the data, displays it and writes it to 

disk, but it also sockets the radar data to RIPS for integration with radiometer, RUC, and surface 

data. RIPS calculates the icing product and generates icing hazard, radiometer, RUC, surface 

met and radar displays in near real time. It archives the data and selected images and serves the 

data via HyperText Transport Protocol (HTTP) to a Web server. In addition the AUX computer 

controls and monitors radar hardware, such as the TWTA transmitter and the Pulse Controller. 

All of the modules which compose the GRADS, except for the AUX computer, run on 

Linux operating systems. They are implemented in object-oriented C, C++, and Java, as a 

number of independent processes that communicate via shared memory, sockets, Remote 

Method Invocation (RMI) and HTTP. The AUX Radar Monitor program uses LabView running 

on Windows 2000 and communicates with RIPS via TCP sockets. 
36

See Appendix F for a complete listing of existing software. 

Figure 17. Software architecture diagram showing information flow. Each block represents a 

different computer. 

3.2.2 GRIDS Computer Architecture 

The GRIDS is designed to distribute data acquisition, data stream integration, processing, 

icing hazard calculation and display functionality across several platforms. It is currently 

designed with five computers, excluding the radiometer computer, which is part of a COTS 

radiometer system, and the off-site archival system/Web server. These five computers are 

connected with a Local Area Network (LAN). More detail on this, including a complete system 

block diagram and a GRADD data flow diagram, is available in Appendix D. The GRADS 

computers are rack-mountable PCs running the Linux operating system; they perform the 

functions of radar control, data acquisition, and data fusion. Another computer, called AUX, is a 

rack-mountable Windows PC that runs the Radar Monitor process that performs additional radar 

control functions and monitors the system health. The last computer is a Linux PC that functions 

as a network firewall. Because of the modest demands made upon it, it could be an older PC. In 

order to allow for flexibility in the chosen radiometer subsystem, the GRIDS software requires 

from the radiometer only a data stream of brightness temperature from the three radiometer 

frequencies, integrated liquid water, integrated water vapor, and the temperature profile. 

radar 

health

radar 

health

met

radiometer 

Web server products 

RUC

raw radar data 

1 Gb/sec 

IEEE488 

radar fields

radar 

control 

DIFRS 

Digital IF Receiver 

Subsystem 

RC+ 

Radar Control Plus 

RIPS 

Remote Integrated 

Product Server 

radar 

triggers 

XRADS 

GRADS radiometer 

data & 

control 

raw 

RUC 

raw 

met

AUX 

Auxiliary Computer 

Windows 2000 

Firewall 

2 IF channels 

Internet
37

It may be possible to combine some of the functions to eliminate one or two of the 

computers and thus reduce the cost. This decision can be made when the system is running and 

the computational loads can be assessed. Because of the way processes communicate, the 

software effort to combine functions is quite small. However, because of the low cost of PCs, 

the simplicity of distributed functions and the redundancy provided by multiple systems, it was 

decided to use five computers in the initial design. 

3.2.2.1 Digital IF Receiver Subsystem (DIFRS) Computer 

The Digital IF Receiver Subsystem, commonly referred to as DIFRS, is a Linux-based 

computer system. It incorporates a two-channel digital receiver card (ICS-554B), a Radar 

Timing Generator (RTG) card, and a frequency synthesizer card. All cards are Commercial-OffThe-Shelf (COTS) products, but the RTG is programmed by ESRL/PSD. The frequency 

synthesizer card is used to generate a needed frequency (80 MHz) for the digital receiver card, 

which takes signals at IF frequency (60 MHz) and digitally produces the baseband signals. 

The RTG card generates various radar timing signals such as the radar trigger, range gates, 

and receiver blanking signals. Since the card uses programmable FPGAs, this allows it to be 

reprogrammed when required. 

Software running on the DIFRS is written in C++ since speed is important here. There is no 

graphical user interface (GUI) used on this computer since its control comes from RC+ via a 

TCP socket. This is purposely done in order to remove the computing load associated with GUIs 

from this machine. 

In time-series or spectrum mode, raw data will be transferred to RC+, but in pulse-pair 

modes, DIFRS will implement the pulse-pair algorithm and send the resulting products to RC+. 

Whether DIFRS or RC+ will implement spectral processing in those modes will be determined 

by computational load. 

3.2.2.2 Radar Control + (RC+) Computer 

The Radar Control+ (known as RC+) computer provides an operator’s graphical interface 

panel for the radar control, acquisition, display and archival of GRIDS. It is Linux-based and 

contains a GPS receiver card and functions as a time base for the entire system. It is on this 

machine that the real-time displays are created, images are captured, and data are written to local 

disks. This machine also runs processes that serve images and data to local client processes, 

which include data archival and the RIPS processes described below. This code is written in 

C++ and uses Motif libraries. When antenna scanning is implemented, the motion controller and 

antenna encoder cards will be interfaced into this machine. For additional detail on the RC+ 

processes, see Section 3.2.3.3 and Appendix G. 

3.2.2.3 Auxiliary Computer (AUX) 

The Auxiliary computer (known as AUX) is a rack-mountable Windows-based computer. It 

performs the functions of monitoring and controlling equipment, and retrieving and preprocessing the data from the RUC model. For additional detail on monitoring, see Section 3.4. 

Almost all programs on this computer are implemented in LabVIEW. 
38

3.2.2.4 RIPS Computer 

The RIPS computer is another Linux PC. This computer integrates and archives data from 

the various sensors and information sources: radar, radiometer, surface meteorological sensors, 

and the RUC model. It performs the icing algorithm and displays all of the raw and derived 

products in graphical format. Code on this machine is written in Java and uses the Abstract 

Window Toolkit (AWT) and Swing Java Foundation Classes. AWT and Swing are Java user 

interface toolkits which are commonly used to develop GUIs in Java. In order to serve data to a 

Web server it needs to be connected to the Internet through a firewall computer. The RIPS 

software and the RC+ software could be run on the same computer, although operational 

experience has shown that separate computers (or at least separate monitors) make viewing the 

numerous displays easier. 

3.2.2.5 Firewall Computer 

The firewall computer acts as a gateway to the Internet for the other GRIDS computers. It 

centralizes security problems and provides system security for the other computers, protecting 

them from hackers. This is a very important function, because it removes the burden of keeping 

the operating systems on the other computers up to current levels. Because of special hardware 

drivers on some of the computers, it is not possible to keep software current without rewriting 

drivers. This computer is also a rack-mountable PC with a Linux operating system. Because it 

is the same style computer as the other PCs, it can also function as a spare or redundant 

computer. 

3.2.3 Radar Data Acquisition and Processing 

3.2.3.1 Data Acquisition 

The radar is a dual-polarization, pulsed Doppler radar with a high duty cycle. A single 

circular polarization is transmitted, and two polarizations, designated main and cross, are 

received simultaneously. The radar receiver provides two channels of analog output (main and 

cross) at the IF frequency of 60 MHz which feed the digital receiver card in the DIFRS 

computer. The digital receiver card’s 14-bit A/D converters are driven by an 80 MHz sampling 

clock from the frequency synthesizer board. The 60 MHz IF frequency and the 80 MHz 

sampling rate results in a signal frequency of 20 MHz in the digital signal. This is digitally 

mixed with a 20 MHz signal from a Numerically Controlled Oscillator (NCO) to deliver a signal 

at baseband. The baseband signal has two channels; they are referred to as I and Q for in-phase 

and quadrature. The signals are digitally filtered to the appropriate bandwidth and are then input 

into the computer’s memory. After filtering, there are about 69 samples (range gates) after each 

radar pulse, depending on the operating mode. Appendix H describes in detail the digital filters 

used in the digital receiver card. 

3.2.3.2 Front-end Processing 

The DIFRS computer performs the calculations described in the GRIDS Covariance 

Algorithms document (Appendix I). Note that there are two classes of results from these 

calculations: recorded data products and display products. Because the display products can be 

recalculated from the recorded data products, they are not recorded. They are calculated solely 

for the purpose of providing real-time displays and as input for the icing algorithm. After the 
39

covariance algorithms have been implemented, we intend to implement spectral processing in 

order to improve the GRIDS algorithm’s performance in mixed-phase clouds. 

These processed data are then sent via 1 Gbit Ethernet to the RC+ computer using TCP 

sockets. 

3.2.3.3 Back-end Processing 

This section describes the processing on the RC+ and RIPS computers. When RC+ receives 

data from the DIFRS computer, it is ingested by a process called readSock. It then calculates 

and appends header information and sends the recorded data products through shared memory to 

another process, writeDisk, which writes these records to disk. At the same time the readSock 

process sends the display data products to another process, xDisplay, which displays the radar 

data. Simultaneously, a relay process sends reflectivity and polarization products to RIPS. 

There are RIPS processes that ingest radar data from RC+, surface data from the AUX 

Radar Monitor, radiometer data from the radiometer computer, and RUC data which have been 

FTPed from the AUX Radar Monitor. Another process calculates the icing hazard every minute 

throughout all portions of detected clouds and sends the results for display. Two additional 

processes display the products in real time. Yet another process posts data to an HTTP server for 

remote display and archival. 

For more details, see Appendix G (GRADS Software Specifications). 

3.2.4 Radiometer Data Acquisition and Processing 

A computer associated with the radiometer calculates liquid water and water vapor column 

amounts and vertical temperature profiles. It then sends the data to the RIPS computer via a TCP 

socket, where the data are ingested, written to disk (one file every hour) and displayed in real 

time by RIPS. 

3.2.5 RUC Model Data Processing 

The Radar Monitor process acquires RUC model data from a Web site approximately once 

an hour and then reformats the data. It then writes time-stamped files on the RIPS internal disk 

via FTP. Separate RIPS modules process, display, and archive those files. 

3.2.6 Surface Met Data Processing 

The Radar Monitor process acquires surface meteorological data from GRIDS sensors and 

sends surface temperature, humidity, and rainfall data to RIPS via a TCP socket. Separate RIPS 

modules read, display, and archive the data stream in real time whenever new data are acquired 

(typically every minute). The logged data are useful for research purposes as well as diagnostic 

purposes, and for detecting water on the radome. 

3.2.7 Icing Algorithm Processing 

There is a RIPS module which accesses the latest radar, radiometer, surface meteorological, 

and RUC data and calculates the icing hazard potential. Once that calculation has been made, 

the numerical information needed to color-code icing hazard displays is sent to the icing display 

module, icingGui. Here data are displayed and communicated externally. Implementation of the 

icing algorithm is discussed more thoroughly in Section 3.3. 
40

3.2.8 XRADS User Interface 

The Extended RADS or XRADS user interface is called RCP (Radar Control Program) and 

runs on the RC+ PC. It communicates with the rest of the GRADS processes in RC+ via shared 

memory. RCP communicates with the GRIDS-AUX and radiometer computers, and the RIPS 

process via TCP sockets. An operator can view, build, modify, check, save, load, and run 

control tables and queues of control tables. It allows the operator to start, stop, and position the 

radar and radiometer antennas (if applicable), to tell the AUX machine to stop sending RUC and 

surface data, and to turn data archival on and off. Status messages and a help menu will be 

available via the user interface. See the GUI prototype in Appendix G. 

For autonomous operation, no operator will be routinely exercising the user interface; 

instead, control tables will be read via configuration files. The user interface will be available 

for intervention (if needed) and to display health and status information to personnel visiting the 

container. 

3.3 Icing Hazard Algorithm Implementation 

3.3.1 Core Algorithm Implementation 

The core icing algorithm has been described in Section 2.4. As noted, it uses four decision 

points based on the slant-path, fixed-beam measurements of liquid water (threshold 

LW > 0.05 mm), radar reflectivity (threshold Ze > -23 dBZ), radar depolarization ratio 

(threshold DRth = -30 ± 2 dB), and on the ingested temperature profile (threshold T < 0oC) to 

identify hazardous regions within the clouds. Table 3 and Figure 18 depict how the thresholds of 

these four parameters are applied in the decision tree used to determine the warning level for 

icing potential as a function of altitude (Hi). 

The icing potential is indicated in a continuous time-altitude display updated every minute 

(as schematically depicted in Figure 1 and in actual application in Figure 13), such that the realtime conditions and a short history (e.g., several hours) are presented as an evolving profile 

through all clouds in the radar beam. The warning of icing potential is scaled as follows: red for 

probable, yellow for caution, green for no threat. The appropriate color, or warning level, 

appears at the time and altitude of the condition. 

This core algorithm is conservative and may over-warn. The performance of the core 

algorithm will be assessed during field trials, and adjustments made later to the decision points, 

if needed. 
41

Table 3. Icing algorithm thresholds 

Condition Red: All four of the thresholds are affirmed. 

Condition Yellow: Only the first three thresholds are met (LW > 0.05 mm, T < 0°C, 

Ze > -23 dBZ), but DR > DRth + 2 dB. This indicates the co-existence of liquid and ice 

particles, a mixed-phase condition. Caution is warranted. 

Condition Green: Clear skies obviously represent the first order condition green. Within 

clouds, green is established if T > 0°C; or if cloud is supercooled (T < 0°C), but either 

LW ≤ 0.05 mm, or Ze ≤ -23 dBZ. Low Ze means that the combination of droplet sizes and 

concentrations are insufficient to produce an icing threat, even if DR ≤ DRth ± 2 dB. For all ice 

clouds DR > DRth and LW < 0.05 mm, establishing condition green by default. 

Figure 18. Core icing hazards algorithm. 

No 

Yes 

Yes—possible hazard

No 

No 

Yes—possible hazard 

No 

Yes—possible hazard 

Begin 

MWR: 

LW > 0.05 mm? 

No hazard at 

any altitude 

i = 0 

Sonde/Model Temp: 

T(Hi) < 0 C? 

Radar Echo: 

Ze(Hi) > -23 dBZ? 

Radar depol threshold: 

DR(Hi) < DRth dB? 

No hazard at 

altitude Hi 

Caution!

Icing possible at 

altitude Hi Alert!

Icing probable 

at altitude Hi 

i = i + 1 

End 
42

3.3.2 Algorithm Enhancements 

As noted previously, the GRIDS algorithm is an evolving decision-making tool, and as such, 

it is expected that many enhancements will be made to it as operational experience is accrued. 

The core algorithm is an effective beginning, with much potential for enhancements. GRIDS can 

gain power with a number of very feasible algorithm upgrades. The overall effect of these 

enhancements is to make GRIDS icing warnings more quantitative and robust. 

The potential enhancements include but are not limited to the following; more detail is 

provided in Appendix B: 

• Liquid water enhancements 

The core algorithm can be enhanced for GRIDS by distributing the value of LW in different 

ways throughout the various portions of detected clouds, using better temperature profiles to 

refine hazard potential, using knowledge of reflectivity bright bands (if detected), and by 

incorporating additional information from the radar and radiometer if and when they point to 

the zenith. In Appendix B we discuss these enhancements in more detail, and relate them to 

icing hazards potentials defined by envelopes in Appendix C of the FAR. 

The core algorithm operates from the detected presence of LW above the defined threshold, 

but the microwave radiometer actually measures the quantity of LW in the tropospheric 

column, and the GRIDS radar measures the cloud boundaries (top and bases), and thus cloud 

layer depth. Therefore, by various methods, the path-integrated liquid measured with the 

radiometer can be assigned to, and, with more sophistication, distributed through the cloud 

layer(s) and converted to a concentration of liquid water (Kg of liquid per m3

 of air, i.e., the 

liquid water concentration or LWC) to better rate icing severity. The supercooled LW can 

then be allocated linearly through the depth of cloud, but adiabatic or climatological LW 

profile shapes have been found to be more realistic (Politovich et al. 1995; Stankov et al. 

1995). A method for making the first steps toward use of the actual LW values to estimate 

LWC and estimate the severity in terms of the FAR Appendix C ratings is outlined in 

Appendix B. 

Methods to combine automated radiometric and radar measurements to profile cloud LW are 

under development, and are promising but not yet ready to apply operationally (e.g., Lohnert 

et al. 2001; Reehorst et al. 2005; Westwater et al. 2006). GRIDS developers should keep 

abreast of these developments. 

• Temperature enhancements 

Within a supercooled cloud, the icing potential increases with temperature, so it is possible to 

rate icing risk to the temperature profile from RUC or other measurements. Steps to add this 

temperature classification feature are noted in Appendix B. 

The tropospheric temperature and dew point soundings, as available from the RUC, will be 

useful since most severe icing occurs between 0°C and -10°C, and thermodynamic instability 

can indicate forcing to produce the condensation of liquid. Also, thermodynamic instability, 

as in embedded convection, can enhance LWC. Various temperature profile scenarios can be 

implemented to provide a temperature instability enhancement. 

The application of 30 and 90 GHz radiometric measurements to estimate the layer-mean LW 

temperature, as described in the section on radiometric temperature profiling (Section 2.3.4), 
43

can be readily implemented with the existing GRIDS design, as a cross-check on the RUC 

temperature profile, especially at low altitudes. 

• Bright band (melting level) enhancement 

Obviously, supercooled cloud liquid can occur only at altitudes where T < 0°C, which will 

begin slightly above the melting level if one occurs. The melting level is detected with 

radars as the bright band. Freezing drizzle and rain occur at the surface when falling 

snowflakes melt, and the droplets they create fall into colder, sub-cooled air nearer the 

surface. The first indicator is evidence of a bright band, the signature of which is much more 

defined in the depolarization measurement than the reflectivity measurement. Thus, 

automated recognition of the bright band can be used to (1) estimate the altitude above which 

cloud liquid is supercooled, (2) cross-check the RUC melting level and correct it if needed, 

(3) identify freezing drizzle or rain when combined with a RUC temperature sounding, and 

(4) recognize rain and potential degradation of GRIDS signals when the rain rate from 

reflectivity is also used. See Appendix B for some approaches to adding these features to the 

core algorithm. 

• Vertically pointing enhancements (depolarization and vertical motion measurements) 

The addition of zenith pointing measurements of DR and the vertical velocity parameters are 

expected to provide significant additional value to the GRIDS measurements, not only by 

enhancing the estimate of the potential icing hazard through better identification of the 

prevailing ice and liquid processes, but also by indicating the vertical atmospheric forcing 

and turbulence that produces those conditions. 

To these ends, more specific identification of the different ice particles by their 

depolarization in itself can add another level of confidence to detection of an icing condition, 

especially in those clouds of mixed phase. The GRIDS capability to do this has been 

thoroughly demonstrated (Reinking et al. 2000a). Also, GRIDS is a Doppler radar, so 

estimates of the beam-radial velocity, Ve, of reflecting hydrometeors are also routinely 

measured. The cloud motion and hydrometeor settling measurements can be greatly 

enhanced using the optional addition of the vertically pointing radar beam. 

Since cloud physics principles dictate that cloud ice particles will rapidly consume liquid 

droplets, the LW component in mixed-phase clouds can only present a lasting icing hazard if 

the dynamics support continued condensation. Mixed-phase icing hazards may occur, 

especially when embedded convection is involved to force continued condensation of new 

liquid in the presence of ice particles. Measurements of vertical motion along with DR, 

including the spectra of vertical motion, can be used to identify mixed-phase clouds through 

the differences between cloud droplet and ice particle fall speeds and to note rain in the 

signature of accelerated fall velocities. This has been demonstrated, as discussed with the 

case study in Section 2.5.1, and in Appendix B. Thus, utilization of the radar’s measurement 

of vertical motion can enhance the algorithm in and without the presence for rain. 

• Rain Recognition Enhancements 

As noted previously, the core icing hazards algorithm does not incorporate rainfall directly 

because the commercially available microwave radiometers integrate a blower system to dry 

a microwave-transparent window that protects the antenna. However, experiments by NASA 

with these drying devices during the 2nd Alliance Icing Research Study (AIRSII 2003) 
44

indicate that unreasonably high values of LW are obtained during significant rain, so this 

window-blower approach is not fail-safe. Drizzle is unlikely to induce false readings 

(Reinking et al. 2000a), but at this writing a rain-rate threshold above which the dryers fail 

has not yet been established. GRIDS can be more stringently required to recognize periods 

of rain, and to define alternate paths for identifying the hazard or to automatically make 

weighting adjustments to other parameters in the icing algorithm during substantial rain 

events. 

Approaches to rain recognition include the following: 1) use of the surface rain-rate sensor, 

2) use of the value and variance of the microwave radiometer LW measurement itself to 

identify unreliable estimates, 3) use of the GRIDS radar measurements reflectivity-rain-rate 

(Z-R) estimate from the GRIDS radar, and 4) use of the Doppler vertical velocity and 

velocity variance to identify times of likely production of high LW when radiometer 

measurements are made uncertain by substantial rain. Due to the short propagation path 

below normal bright bands in winter precipitation, attenuation of the radar’s signal by rain is 

minimized. These approaches and factors are examined in Appendix B. 

• GRIDS-CIP Synergy 

The noted enhancements to the GRIDS algorithm begin a list that will add to the power of 

GRIDS. As new developments in instrumentation, measurements, and modeling that could 

support GRIDS come online, they would logically be examined and incorporated as 

appropriate. Integration of the Current Icing Product (CIP; Bernstein and Schneider 2004) 

could be one such strong addition. The CIP integrates several network data products to 

produce mapped estimates of icing and SLD potential. GRIDS and CIP are each a standalone system, but together the power for identifying the real icing potential can be greatly 

enhanced. CIP is explained and this is discussed further in Appendix B. 

3.3.3 Important GRIDS By-products 

It should be evident at this point that the many radar and radiometric measurements made by 

the GRIDS multi-sensor system have numerous applications to broader weather forecasting. 

With certainty, this will greatly increase the GRIDS benefit-to-cost ratio. Some of the GRIDS 

by-products—precipitable water, cloud boundaries and thicknesses, multiple ceilings, turbulence 

from radial velocity variance, detailed cloud microphysics, all in data streams suitable for 

integration into the national network—can support many fundamental aspects of National 

Weather Service operations if implemented. Assimilation of these data into the network 

modeling product, and continuous measurement of all these parameters through the depth of the 

troposphere for the weather and climate assessment databases, provide not-to-be missed 

opportunities. 

3.4 Monitoring and Calibration 

The monitoring and calibration functions are largely performed by the Radar Monitor 

process which will run on the Auxiliary Computer (AUX). Several analog and digital signals 

will be monitored, a radar health status log will be generated, and certain radar functions will be 

controlled either under human control, or automatically in response to a radar system or power 

failure. 
45

Industrial Packs (IPs) will be used to interface the radar system to the AUX system. IPs are 

small modular circuit boards that each perform a specific function. Four IPs are mounted on an 

IP carrier board which occupies one slot on the PCI backplane. This technology allows the 

system designer to “mix and match” multiple I/O functions while using a minimum number of 

backplane slots. In the AUX system, an A/D IP will be used to sample analog signals and digital 

I/O IPs will be used to sample and drive TTL-level digital signals. Future I/O requirements can 

be easily addressed by adding IPs with whatever functionality is needed. 

Several analog signals will be sampled hourly. Acceptable operating ranges will be 

established for each of these signals. Any readings that are outside the acceptable operating 

range will be documented and time-stamped in a status file that will be updated daily at 

12:01 AM. Criteria will be established for taking action based on readings that are out of range, 

ranging from simple reporting within 24 hours to immediate shutdown of sub-systems or the 

entire GRIDS. For example, the radar transmitter may be automatically shut down if certain 

power-supply failures are detected. The analog signals which will be monitored are listed in 

Table 4. 

The Radar Monitor process is written in LabVIEW, which is a graphical programming 

language designed for instrumentation control. 

Table 4. Analog Signals monitored by Radar Monitor process 

CH # I/O connector pin # Signal Name Units 

1 1,2 Transmitted RF power watts 

2 27,28 Outdoor temperature °C 

3 3,4 Outdoor humidity % 

4 29,30 Indoor temperature °C 

5 5,6 Indoor humidity % 

6 31,32 Pulse controller +5V #1 volts 

7 7,8 Pulse controller +5V #2 volts 

8 33,34 Pulse controller +28V volts 

9 9,10 Pulse controller +15V volts 

10 35,36 Pulse controller –15V volts 

11 11,12 RF CUD +12V (mon) volts 

12 37,38 RF CUD TX +12V (mon) volts 

13 13,14 RF CUD RX +12V (mon) volts 

14 39,40 IF REC/MOD +12V (mon) volts 

15 15,16 IF REC/MOD +15V volts 

16 41,42 Temp, circulator #1 °C 

Digital signals to be monitored include phase-lock alarms on the IF frequency phase-locked 

oscillators (PLO’s). A course of action based on these conditions will be chosen and 

implemented. For example, the radar transmitter might be automatically turned off if there is a 

PLO failure. 

Software from the UPS manufacturer will be used to monitor the incoming line power. In 

the event of a power failure, the Radar Monitor process will turn off the radar, radiometer, and 

computers in the proper sequence, before UPS power is depleted. When power is restored, the 

entire system will be restarted automatically and resume normal operations. 
46

Radar and radiometer calibrations will be performed both manually and automatically. Full 

calibrations will be done manually at an appropriate frequency (about once per year, based on 

our many years’ experience with such systems), or whenever failure of components dictates a 

recalibration. An automated verification of calibration stability will be performed daily. For this 

verification, two measurements will be made. First, the noise level of the receiver channel will 

be measured under conditions of zero transmitter power. Also, a known level of white noise will 

be injected into the radar receiver to produce an output near the middle of the receiver’s dynamic 

range. Both noise levels will be recorded and compared with recent and historical values. If 

both values remain stable, it can be reasonably assumed that the overall receiver calibration is 

unchanged. To make this daily check, a digital I/O IP will be used to drive the necessary digital 

control signals, switching circulators and attenuators into the correct state and turning on the 

calibration noise diode. 

3.5 Communication 

GRIDS uses an Internet connection to send data from the on-site GRIDS computers to an 

off-site Web server and to receive RUC data. It uses a Local Area Network (LAN) to 

communicate between the various on-site GRIDS computers. 

3.5.1 Internet 

GRIDS should have a broadband Internet connection for best performance, although use of 

telephone lines is probably acceptable for experimental purposes. An Internet connection is 

essential for receiving the RUC data and for providing real-time data to off-site users. 

RIPS will send a Graphical Interchange Format (GIF) file of the most recent icing hazard 

display via Secure Copy Program (SCP) or HTTP to the Boulder Web server for display on the 

Web. Typically, these files will be updated and sent once per minute. 

RIPS will also archive radar, radiometer, RUC, surface meteorological and icing data via 

SCP to the archival system in Boulder, typically once per hour. 

On the AUX system, the Radar Monitor process will download RUC temperature profile 

data from the Web as data sets become available. Typically, these data sets are updated once per 

hour. 

RIPS will be use the HTTP to transfer data to the home Web server. Several of the 

components of RIPS working with the HTTP server will serve displays similar to the ones seen 

in the radar on the Web where they can be viewed with a browser. 

A Linux firewall computer will protect the internal GRADS (and other) systems local area 

network (LAN) from the external network by providing centralized network security. 

3.5.2 Local Area Network (LAN) 

The LAN has two components: a dedicated 1 Gbit/sec link between the DIFRS computer 

and the RC+ computer, and a 100 Mbit/sec Ethernet link that connects the RC+ computer to the 

rest of the GRIDS computers (Figure 15 and Figure 17). 

The following describes the primary information flow between the computers. 

1) DIFRS sends raw radar data to RC+ over a dedicated 1 Gbit/sec Ethernet link. 
47

2) RC+ sends radar control commands to the Radar Monitor process on AUX and receives 

radar status (health) messages from it. These commands are used to turn the radar 

transmitter on and off, initiate receiver calibration sequences, etc. The radar status 

messages are passed along to RIPS. RC+ also processes the raw radar data and produces 

meteorological fields that are passed along to RIPS. 

3) RIPS integrates all the data streams: radar fields, RUC data, surface met data and 

radiometer data. It then displays and archives the data locally. As mentioned 

previously, RIPS also sends the data across the Internet to the off-site computers where it 

is served to the Internet at large. 

4) RIPS will send the Radar Monitor process on the AUX computer commands to start and 

stop the RUC data acquisition. The Radar Monitor process uses the local area network 

to send RUC data, surface met data, and system health data to RIPS. 

5) The AUX system will send radar system status and error messages to RC+ via a TCP 

socket. Typical messages might notify the RC+ that the radar has been successfully 

turned on, or that a hardware failure has been detected and that XRADS should stop data 

acquisition. 

6) UPS software will run on the GRADS, AUX, and the Radiometer PCs to monitor the 

UPS over a network connection. When a power failure occurs, this software will direct 

all five of the operating systems to shut down gracefully. This software will also be used 

to send an automatic e-mail to notify selected recipients of the problem. 

3.5.3 IEEE-488 Bus 

The AUX system will communicate with the TWTA and the Pulse Controller via the IEEE488 bus. The Radar Monitor process will be used to send commands to the TWTA and the Pulse 

Controller and monitor their status. 

3.6 Container 

The GRIDS prototype will be built into a dedicated, transportable container, so that it can be 

constructed in one location and demonstrated in other locations. Because of common practice in 

the transportation and shipping industry, a standard ISO shipping container (seatainer) will be 

used for this purpose, with nominal dimensions of 20’L x 8’W x 8’H. This rugged steel-shelled 

container will also serve as a rigid mount for the GRIDS antenna and radiometer. It will be fitted 

with insulated walls, environmental controls, and an extensive electrical distribution system, thus 

providing laboratory-like conditions in which to operate. Although this system will be 

unattended when it is fully developed, there is still a need to make it roomy and pleasant for both 

visitors and project or operations staff. Below we show the floor plan for the GRIDS container 

(Figure 19), and an external side view depicting the antenna and radiometer mounted to the 

container (Figure 20). 
48

Figure 19. Plan view of GRIDS container showing location of key equipment. Note vestibule 

entry on the right. 

Figure 20. Side view of container showing 3-m antenna with radome pointing at 40o elevation 

angle, and mailbox radiometer mounted on an opposing side. 

Future versions of the GRIDS container could be made considerably smaller, since they will 

not need to accommodate development staff. 
49

3.7 Off-Site Computer Systems 

As previously mentioned, certain products created by RIPS, as well as raw data from the 

sensors, are shipped from the RIPS machine to a Web server machine in Boulder for Web 

browser-accessible displays and archival in close to real time. Scripts can be used to send data 

and images back periodically, but to allow for real-time updating and viewing of data, a more 

sophisticated architecture is necessary. This part of the GRIDS is described below. 

3.7.1 Software Architecture 

The off-site software architecture consists of one centralized server called DataRexx, at least 

one reformatter for each data source, a relay (or fan-out box) for each data source, and various 

data viewers and archivers, as shown in Figure 21. Briefly, DataRexx is a software product 

licensed from Bear Peak Software, Inc., that allows multiple remote data producer clients to 

“post” data to it via HTTP. Data consumer clients can then “get” data from DataRexx via HTTP. 

Data reformatters receive remote raw data and package it into archival format. Data relays each 

input a single data stream and deliver that data stream to multiple clients. Data viewers allow the 

user to view and manipulate the data, while archivers write both the archival data and products to 

disk for playback. Since many of the capabilities available on the on-site computers are also 

desirable on the off-site computer, reuse of classes and methods and leveraging on-site processes 

has helped to decrease the time needed for the software development process. For detailed 

information about the off-site software system, see Appendix G. 

Figure 21. DataRexx server diagram showing data sources, server, and data clients. 
50

3.7.2 Computer Architecture 

The Web server machine, located in Boulder, CO, is a Linux-based rack-mount machine 

with three terabytes of disk space. This allows for multiple large data sets to be archived in close 

to real time. 

4. SUMMARY 

In-flight icing annually causes high loss of life and property, and also a high cost for flight 

delays, cancellations, and re-routings caused by icing concerns that are perhaps needless. 

Therefore, the nation has a great need to improve its ability to forecast and observe in-flight icing 

conditions. A preponderance of icing encounters occurs in the vicinity of major airports, where 

aircraft must fly holding patterns in poor weather before landing. In partnership with the Federal 

Aviation Administration (FAA), NOAA’s Environmental Technology Laboratory (ETL) has for 

the past two decades conducted research to develop a practical remote sensing system to help 

improve our ability to detect hazardous icing conditions aloft, and to provide accurate and timely 

assessment of local icing conditions. We call this system the Ground-based Remote Icing 

Detection System (GRIDS). This document specifies the scientific basis and the design for this 

system. 

Our research has established that a radar measurement of the polarized scattering properties 

of clouds can be used to distinguish between two fundamental categories of cloud particles—ice 

and liquid. It is liquid water particles that present an icing hazard, but they must be super-cooled 

and have sufficient water content to present a real hazard. GRIDS provides those measurements 

and is designed for unattended operation. It will observe nearby clouds continuously, measure 

the amount of liquid water they contain, and determine if hazardous super-cooled droplets are 

present, for every portion of the cloud from its base to an altitude of 10 km AGL. To guarantee 

that even weak icing conditions high in clouds are detected, GRIDS must employ the most 

sensitive civilian cloud radar yet built. This is not a fundamental problem because the 

components are all commercially available. 

In summation, through careful design based on new and sound remote sensing theory and 

field-tested proof of concept, the GRIDS technique has been demonstrated as an effective tool 

for real-time monitoring of hazardous aircraft icing conditions. It is intended for use within the 

airspace of major airports, and its continuous output can be linked to enhance, anchor, and verify 

larger-area forecasts from the now-operational Current Icing Potential model and other icing 

products. 

GRIDS combines information from a “cloud” radar and a microwave radiometer, each 

developed by ETL, and by ingesting local temperature profiles from a reliable source (National 

Weather Service). GRIDS is a low-risk endeavor. It is an exercise in optimally configuring 

existing technology based on proven science, establishing a robust means for unified operation of 

the sensors, combining measurements with Web-based data automatically, and issuing real-time, 

easy-to-interpret assessments of icing hazard potential via the Internet. GRIDS will be housed in 

a transportable container. It requires only power, a broadband Internet connection, and a clear 

field of view. 

Finally, it should be evident at this point that the many radar and radiometric measurements 

made by the GRIDS multi-sensor system have numerous applications to broader weather 
51

forecasting. With certainty, this will greatly increase the GRIDS benefit-to-cost ratio. Some of 

the GRIDS by-products—precipitable water, cloud boundaries and thicknesses, multiple ceilings, 

turbulence from radial velocity variance, essential and specific cloud microphysics well beyond 

ice vs. liquid, all in continuous data streams suitable for integration into the national network—

can support many fundamental aspects of National Weather Service operations if implemented. 

Assimilation of these data into the network modeling product, and continuous measurement of 

all these parameters through the depth of the troposphere for the weather and climate assessment 

databases, provide not-to-be missed opportunities. GRIDS can be employed as a multi-function 

instrument. 

5. ACKNOWLEDGMENTS 

The FAA has supported ETL (now integrated with ESRL) in its development of GRIDS, 

including this design work. The views expressed are those of the authors and do not necessarily 

represent the official policy or position of the FAA. Much of this report was based on a GRIDS 

Preliminary Design Review (NOAA ETL Radar Meteorology and Oceanography Division, 

2001), which was written by M. J. Post, Roger Reinking, Sergey Matrosov, Carroll Campbell 

and Janet Gibson, with support from others in the division. 

The authors of this report gratefully acknowledge all contributors to that PDR and the very 

major contributions of the following people to the entire research and development effort: Bruce 

Bartram, Kurt Clark, Duane Hazen, Robert Kropfli, Sergey Matrosov, Ken Moran, M.J. Post, 

and Geoff Thompson. These people contributed in diverse ways but this work would not have 

been possible without their leadership, support, and outstanding expertise. 
52

6. REFERENCES 

The following references are in alphabetical order. For a list of ETL references in 

chronological order, illustrating the history of the development of GRIDS, see Appendix A. 

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Barnes, Stanley L, 1980: Report on a meeting to establish a common Doppler radar data 

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Benjamin, S.G., D. Devenyi, S.S. Weygandt, K.J. Brundage, J.M. Brown, G.A. Grell, D. Kim, 

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Bernstein, B.C., and T.L. Schneider, 2004: Two perspectives on a Colorado icing event: GRIDS 

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October. Amer. Meteorol. Soc., Boston. Available on CD from the AMS. 

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53

Cober, S. G., G. Isaac, and J. W. Strapp, 1995: Aircraft icing measurements in east coast winter 

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2788-2799. 

Gibson, J.S., W.C. Campbell, B.E. Martner, 2004: RADS: A radar acquisition and display 

system for research radars. NOAA Tech. Report. OAR ETL-69, NOAA Office of Oceanic 

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Heggli, M.F., and R.M. Rauber, 1988: The characteristics and evolution of supercooled liquid 

water in wintertime storms over the Sierra Nevada: A summary of microwave radiometric 

measurements taken during the Sierra Cooperative Pilot Project. J. Appl. Meteor., 27, 989-

1015. 

Hogg, D.C., F.O. Guiraud, J.B. Snider, M.T. Decker, and E.R. Westwater, 1983: A steerable 

dual-channel microwave radiometer for measurement of water vapor and liquid in the 

troposphere. J. Clim. Appl. Meteorol., 22, 789-806. 

Klimowski, B.A., R. Becker, E.A. Betterton, R. Bruintjes, T. L. Clark, W.D. Hall, B.W. Orr, R. 

A. Kropfli, P. Piironen, R.F. Reinking, D. Sundie, and T. Uttal, 1998: The 1995 Arizona 

Program: Toward a better understanding of winter storm precipitation development in 

mountainous terrain. Bull. Amer. Meteor. Soc., 79, 799-813. 

Koldaev, A.V., E.N. Kadygrov, A.V. Troitsky, and J.W. Strapp, 1998: Remote sensing of 

average liquid water content layer height in winter clouds. Proc. 8th Atmospheric 

Radiation Measurement (ARM) science team meeting, U.S. Dept of Energy, 381-385. 

Korolev, A.V., G.A. Isaac, S.G. Cober, J.W. Strapp, and J. Hallett, 2003: Microphysical 

characterization of mixed-phase clouds. Quart. J. Roy. Meteor. Soc., 129, 39-65. 

Kropfli, R.A., and R.D. Kelly, 1996: Meteorological research applications of mm-wave radar. 

Meteorol. and Atmos. Phys., 59, 105-121. 
54

Kropfli, R.A., S.Y. Matrosov, T. Uttal, B.W.Orr, A.S. Frisch, K.A. Clark, B.W.Bartram, 

R.F.Reinking, J.B. Snider, and B.E. Martner, 1995: Cloud physics studies with 8 mm 

wavelength radar. Special issue on cloud microphysics and applications to global climate 

change, Atmospheric Research, 35, 299-313. 

Lohnert, U., S. Crewell, A. Macke, and C. Simmer, 2001: Profiling cloud liquid water by 

combining active and passive microwave measurements with cloud statistics. J. Atmos. 

Oceanic Tech., 18, 1354-1366. 

Martin, L. C. Matzler, T.J. Kewison, and D. Ruffieux, 2006: Intercomparison of integrated 

water vapor measurements. Meterologische Zeit., 15, 57-64. 

Martner, B.E., and R.A. Kropfli, 1993: Observations of multi-layered clouds using K-band 

radar. Paper AIAA 93-0394, Reprints, 31st Aerospace Sciences Mtg., Reno, AIAA, 8 pp. 

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1991: Progress report on analysis of 

differential attenuation radar data obtained during WISP-91. NOAA Tech. Memo., ERL 

WPL-215, Boulder, CO, 43 pp. 

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1993a: Dual-wavelength differential 

attenuation radar measurements of cloud liquid water content. Preprints, 26th Intl Conf. 

on Radar Meteor., AMS, Norman, OK, 596-598. 

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1993b: Cloud liquid water content 

measurement tests using dual-wavelength radar. NOAA Tech. Memo. ERL ETL-235, 

NOAA Environmental Research Laboratories, Boulder, CO, 47 pp. 

Martner, B.E., D.A. Hazen, K.P. Moran, T. Uttal, M.J. Post, and W.B. Madsen, 2002a: 

NOAA/ETL’s vertical-profiling cloud radar and radiometer package. 6th Symp. on 

Integrated Observing Systems, Jan 2002, Orlando, FL, Amer. Meteor. Soc. 

Martner, B.E., B.W. Bartram, J.S. Gibson, W.C. Campbell, R.F. Reinking, and S.Y. Matrosov, 

2002b: An overview of NOAA/ETL’s scanning Ka-band cloud radar. 16th Conf. on 

Hydrology. Jan 2002, Orlando, FL, Amer. Meteor. Soc. 

Matrosov, S. Y., 1991a: Theoretical study of radar polarization parameters obtained from cirrus 

clouds. J. Atmos. Sci., 48, 1062-1070. 

Matrosov, S. Y., 1991b: Prospects for the measurement of ice cloud particle shape and 

orientation with elliptically polarized radar signal. Radio Sci., 26, 847-856. 

Matrosov, S.Y., and R.A. Kropfli, 1993: Cirrus cloud studies with elliptically polarized Ka-band 

radar signals: A suggested approach. J. Atmos. Oceanic. Tech., 10, 684-692. 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, and B.W. Bartram, 1995: Identification of ice 

hydrometeor types with elliptical polarization radar measurements. Preprints, 27th 

Conference on Radar Meteorology, 9-13 October 1995, Vail, CO. Amer. Meteor. Soc., 

Boston, MA, 539-541. 
55

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, and B.W. Bartram, 1996a: Estimation of 

hydrometeor shapes and types from radar polarization measurements. J. Atmos. Oceanic 

Tech., 13, 85-96. 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, B.E. Martner, and B.W. Bartram, 2001: On the use 

of radar depolarization ratios for estimating shapes of ice hydrometeors in winter clouds. 

J. Appl. Meteorol., 40, 479-490. 

Matrosov, S.Y., R.F. Reinking, and I.V. Djalalova, 2005: Inferring fall attitudes of pristine 

dendritic crystals from polarimetric radar data. J. Atmos. Sci., 62, 241-250. 

McDonough, F. and B.C. Bernstein, 1999: Combining satellite, radar, and surface observations 

with model data to create a better aircraft icing diagnosis. Preprints, 8th Conference on 

Aviation, Range and Aerospace Meteorology, Dallas, TX, Amer. Meteor. Soc., 467-471. 

Moran, K.P., B.E. Martner, M.J. Post, R.A. Kropfli, D.C. Welsh, and K.B. Widener, 1998: An 

unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79, 

443- 455. 

NOAA ETL Radar Meteorology and Oceanography Division, 2001: Preliminary Design, 

Ground-based Remote Icing Detection System (GRIDS). Report to the FAA Aviation 

Weather Research Program, In-Flight Icing PDT #4, Task 01.4.3.3, FAA Icing Remote 

Sensing Testbed, Deliverable 01.4.3.1.E3. 65 pp + appendices. 

Paull, G. and E. Hagy, 1999: Historical overview of in-flight icing accidents. TR-9854/01-1 

MCR Federal, Inc. Bedford MA 01730. 43 pp. 

Politovich, M. K., 1989: Aircraft icing caused by large supercooled droplets. J. Appl. Meteor., 

28, 856-868. 

Politovich, M. K, 1996: Response of a research aircraft to icing and evaluation of Severity 

Indices. J. Aircraft, 33, 291-297. 

Politovich, M.K., B.B. Stankov, and B.E. Martner, 1995: Determination of liquid water altitudes 

using combined remote sensors. J. Appl. Meteor., 35, 2060-2075. 

Popa Fotino, I.A., J.A. Schroeder, and M.T. Decker, 1986: Ground-based detection of aircraft 

icing conditions using microwave radiometers. IEEE Trans. Geosci. Remote Sens., GE24, 6, 975-982.

Reehorst, A. L., D. J. Brinker, and T. P. Ratvasky, 2005: NASA icing remote sensing system 

comparisons from AIRSII. AAIA-2005-0253, 10 pp. 

Reinking, R.F., 1998: Detection of ice hydrometeors and freezing drizzle with Ka-band dualpolarization radar. Conf. on Cloud Physics. 17-21 August 1998, Everett, Washington. 

Amer. Met. Soc., Boston. pp 249-252. 
56

Reinking, R.F., and R.A. Kropfli, 2000: Breakthroughs in radar polarization technology and 

related studies. Radar-based Icing Diagnosis Techniques, In-flight Icing PDT Technical 

Direction FY2000 Year-end Task Progress Report to the FAA, Deliverable 00.4.3.E1. 

NOAA Environmental Technology Laboratory. 16 pp. 

Reinking, R.F., R.Caiazza., R.A. Kropfli, B.W. Orr, B.E. Martner, T.A. Niziol, G.P. Byrd, R.S. 

Penc, R.J. Zamora, J.B. Snider, R.J. Ballentine, A.J. Stamm, C.D. Bedford, P. Joe, and A.J. 

Koscielny, 1993a. The Lake Ontario Winter Storms (LOWS) Project. Cover article, Bull. 

Amer. Meteor. Soc., 74, 1828-1829. 

Reinking, R.F. B.W. Orr, B. Stankov, and C. Davis, 1993b: NOAA Ka-band cloud-sensing radar 

measurements from WISPIT. Preprints, 5th Int’l. Conf. on Aviation Weather Systems, 2-6 

August 1993, Vienna, VA. Amer. Meteor. Soc., Boston, MA, 130-134. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, R.A. Kropfli, and B.W. Bartram, 1995a: 

Hydrometeor identification with millimeter-wave dual-polarization radar. Conf. on Cloud 

Physics, 15-20 January 1995, Dallas, Texas. Amer. Meteor. Soc., Boston, MA, 20-25. 

Reinking, R.F., S.Y. Matrosov, R.J. Bruintjes, B.E. Martner, and R.A. Kropfli, 1995b: Further 

comparison of experimental and theoretical radar polarization signatures due to ice 

hydrometeor shape. Preprints, 27th Conference on Radar Meteorology, 9-13 October 

1995, Vail, CO. American Meteorological Society, Boston, MA, 47-49. 

Reinking, R.F. S.Y. Matrosov, and R.T. Bruintjes, 1996a: Hydrometeor identification with 

elliptical polarization radar: Applications for glaciogenic cloud seeding. Cover article, J. 

Wea. Modif., 28, 6-18. 

Reinking, R.F., S.Y. Matrosov, and R.T. Bruintjes, 1996b: Ice and water hydrometeor 

identification with elliptical polarization radar: Applications to precipitation enhancement. 

Preprints, 13th Conf. on Planned & Inadvertent Weather Modification, 28 January-2 

February, 1996, Atlanta, GA. Amer. Meteor. Soc., Boston, MA, 172-179. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner and R.A. Kropfli, 1996c: Differentiation of 

freezing drizzle from ice hydrometeors and rain with polarization radar. Pro., FAA 

International Conference on Aircraft Inflight Icing. 6-8 May 1996, Springfield, VA, II, 

331-338. [Available from NTIS, Springfield, VA 22161. DOT/FAA/AR-96/81]. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner and R.A. Kropfli, 1996d: Differentiation of 

freezing drizzle from ice hydrometeors with polarization radar. Pro., 12th International 

Conference on Clouds and Precipitation. 19-23 August 1996, Zurich, Switzerland. 

International Commission on Clouds and Precipitation (ICCP); I, 446-449. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, R.A. Kropfli, and B.W. Bartram, 1996e: 

Hydrometeor identification with millimeter-wave dual-polarization radar. Proc. ETL/CSU 

Cloud-related Process Modeling and Measurement Workshop, Boulder, Colorado, 23-25 

October 1995. Environmental Technology Laboratory, U.S. Dept. of Commerce, 209-214. 
57

Reinking, R.F., S.Y. Matrosov, R.T. Bruintjes, and B.E. Martner, 1997a: Identification of 

hydrometeors with elliptical and linear polarization Ka-band radar. J. Appl. Meteor., 36, 

322-339. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, and R.A. Kropfli, 1997b: Differentiation of 

freezing drizzle from ice hydrometeors and freezing rain with dual-polarization radar. J. 

Aircraft, 34, 778-784. 

Reinking, R.F., J.B. Snider, and J.L. Coen, 2000a: Influences of storm-embedded orographic 

gravity waves on cloud liquid water and precipitation. J. Appl. Meteor., 39, 733-759. 

Reinking, R.F., B.W. Orr, L. R. Bissonnette, G. Roy, and A. S. Frisch and S. Y. Matrosov, 

2000b: Remote sensing of cloud droplets during MWISP. Pro., Int’l. Geoscience and 

Remote Sensing Symposium (IGARSS 2000). Honolulu, HI, 24-28 July 2000. Pp 187-189. 

(Available from customer.services@ieee.org). 

Reinking, R.F., S.Y. Matrosov, B.W. Bartram, and R.A. Kropfli, 2000c: Evaluation of a slant45-quasi-linear polarization state for distinguishing among drizzle droplets and quasispherical ice particles. Preprints, Int’l. Conf. on Cloud Physics, Reno, NV, 14-18 Aug 

2000. Pp. 243-246. (Avail. from Desert Research Inst., 2215 Raggio Parkway, Reno, NV.) 

Reinking, R.F., R.A. Kropfli, S.Y. Matrosov, W.C. Campbell, M.J. Post, D.A. Hazen, J.S. 

Gibson, K.P. Moran, and B.E. Martner, 2001a: Concept and Design of a Pilot 

Demonstration Ground-based Remote Icing Detection System. Pro. 30th Int’l. Conf. on 

Radar Meteorology, 19-24 July, Munich. Amer. Meteor. Soc., Boston, 199-201. 

Reinking, R.F., R.A. Kropfli, S.Y. Matrosov, W.C. Campbell, M.J. Post, D.A. Hazen, J.S. 

Gibson, K.P. Moran, B.E. Martner, and T. Schneider, 2001b: Concept and Design of a 

Pilot Demonstration Ground-based Remote Icing Detection System. Proc. Conf. on 

Battlespace Atmospheric and Cloud Impacts on Military Operations (BACIMO). July, Ft. 

Collins, CO. 

Reinking, R.F., S.Y. Matrosov, B.W. Bartram, and R.A. Kropfli, 2002: Evaluation of a 45°-slant 

quasi-linear radar polarization state for distinguishing drizzle droplets, pristine ice crystals, 

and less regular ice particles. J. Atmos. Oceanic Tech., 19, 296-321. 

Riley, J., and B. Horn, 1996: Plenary sessions. Proc. FAA Intn’l. Conf. Aircraft Inflight Icing. 

DOT/FAA/AR-96/81, I, 263 pp. (Available from NTIS, Springfield VA 22161.) 

Riley, J.T. and R. McDowall (eds.), 1998: Proc. FAA specialists workshop on mixed-phase and 

glaciated conditions, 2-3 Dec 1998. FAA Tech. Center, Atlantic City, NJ. 346 pp. 

Ryerson, C.C., M.K. Politovich, K.L. Rancourt, G.G. Koenig, R.F. Reinking, and D.R. Miller, 

2000: Mt. Washington Icing Sensors Project: Conduct and Preliminary Results. Paper # 

AAIA-2000-0488, Preprints, 38th AAIA Aerospace Sciences Meeting and Exhibit, 10-13 

January 1999, Reno, NV, 10 pp. 
58

Sand, W. R., A. Cooper, M. K. Politovich, and D. L. Veal, 1984: Icing conditions encountered 

by a research aircraft. J. Clim. Appl. Meteor., 1427-1439. 

Schneider, T.L. and W.C. Campbell, 2006: A ground-based remote icing detection system 

(GRIDS) perspective on the November 11, 2003 SLD case during AIRS2. 12th Conf. on 

Aviation, Range and Aerospace Meteorology, Atlanta, GA, Amer. Meteorol. Soc., January 

2006, 7B.4. 

Schneider, T.L., S.Y. Matrosov, M.D. Shupe, B.W. Bartram, W.C. Campbell, J.S. Gibson, D.A. 

Hazen, and R.F. Reinking, 2004: An overview of ground-based remote sensing during 

AIRS-2 and WISP-04, using the NOAA GRIDS system. 11th Conf. on Aviation, Range 

and Aerospace Meteorology, Hyannis MA, 11-14 October 2004. Amer. Meteorol. Soc., 

Boston. Available on CD from the AMS. 

Schneider, T.L., B.C. Bernstein, and R.F. Reinking, 2005: Forecasting and Ground-based 

Remote Icing-Detection System (GRIDS) documentation of the transition from a glaciated 

environment to an SLD icing environment. 43rd Aerospace Sciences Meeting & Exhibit, 

10-13 January 2005, Reno, NV. American Institute of Aeronautics and Astronautics, 

Washington, D.C.. Paper AIAA 2005-0257, 7 pp. 

Schultz, P., and M.K. Politovich, 1992: Toward the improvement of aircraft-icing forecasts for 

the continental united States. Wea. Forecast., 7, 491-500. 

Stankov, B.B., B.E. Martner, J.A. Schroeder, M.K. Politovich, and J.A. Cole, 1993: Liquid 

water and water vapor profiling in multi-layered clouds using combined remote sensors. 

Paper AIAA 93-0395, Reprints, 31st Aerospace Sciences Mtg., Reno, NV, AIAA, 8 pp. 

Stankov, B. B., B. E. Martner, and M. K. Politovich, 1995: Moisture profiling of the cloudy 

winter atmosphere using combined remote sensors. J. Atmos. Oceanic Tech., 12, 488-510. 

Vivekanandan, J., B.E. Martner, M.K. Politovich, and G. Zhang, 1999: Aircraft icing detection 

using dual-wavelength radar observations. IEEE Trans. Geosci. Remote Sens., 2325-2334. 

Westwater, E.R., 1972: Microwave emission from clouds. NOAA TR ERL 219-WPL 18, 43 pp. 

[Avail. from Supt. of Documents, U.S. Gov’t. Printing Office, Washington, DC 20402.] 

Westwater, E.R., S. Crewell, and C. Matzler, 2005: Surface-based microwave and millimeter 

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59

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Meteor. Soc., 80, 389-406. 
Appendix A 

A-1 

History of Icing Hazards Research at NOAA/ETL

Table 1. NOAA/ETL History of instrument development for detection of icing hazards. 

Instruments Started Use, Tests, Key References

Microwave Radiometer 1983 Measure vertically integrated liquid water path; correlate 

with icing pireps and aircraft incidents (e.g., Popa Fotino et 

al. 1986; Stankov et al. 1993)1

.

Cloud Radar 1991 Determine cloud layer altitudes and internal structure with 

high resolution (e.g., Martner and Kropfli 1993).

Microwave Radiometer 

+ Cloud Radar

1993 Estimate vertical profile of liquid within cloud layers; 

compare with aircraft soundings of liquid (Politovich et al. 

1995; Frisch et al. 1995).

Unattended, Zenithpointing Cloud Radar 1996 Continuous monitoring of altitudes and internal structure of 

clouds overhead (Moran et al. 1998; Martner et al. 2002a).

Dual-wavelength Radar 1991 Measure liquid water content of clouds and map it in 3-D; 

comparisons with liquid path of steerable microwave 

radiometer (Martner et al. 1991, 1993a,b).

Dual-polarization Cloud 

Radar

1991 Identify hydrometeor types; comparison with scattering 

theory and in situ particle sampling (e.g., Matrosov 1991a,b; 

Matrosov et al. 1996; Reinking et al. 1997a,b; Matrosov et 

al. 2001; Reinking and Kropfli 2000, Reinking et al. 2002; 

Martner et al. 2002b; Matrosov et al. 2005)

ETL’s earliest icing research used ground-based dual-frequency (near 20- or 23-GHz and 

31- GHz) microwave radiometers to monitor the liquid water path overhead and correlated those 

measurements with nearby pilot reports (Westwater 1972; Hogg et al. 1983; Popa Fotino et al. 

1986; Stankov et al. 1993). A few years later, ETL developed a scanning millimeter-wave 

Doppler cloud radar (35-GHz, Ka-band) which has evolved into a powerful research tool (Kropfli 

et al. 1995; Kropfli and Kelly 1996). This radar uses a much shorter wavelength (8.66 mm) than 

conventional weather surveillance radars (typically 5 or 10 cm), to more readily detect tiny cloud 

droplets and ice crystals, in addition to the larger raindrops and snowflakes. Thus clouds 

themselves, and not just precipitation, are observed. 

Vertically pointing cloud radar delineates cloud layer boundaries and internal reflectivity 

and velocity structure with remarkable detail, accurately defining cloudy and cloud-free (hence, 

icing-free) altitudes aloft (Martner and Kropfli 1993). A logical step was to combine the 

radiometer’s path-integrated liquid measurements with the cloud radar’s range-resolved 

observations of cloud structure and boundaries, to allow vertical profiles of liquid within cloud 

layers to be estimated (Politovich et al. 1995; Frisch et al. 1995). 

Unattended, continuous monitoring of cloud layer heights and structure became available in 

1996 with ETL’s development of a vertically pointing version of the scanning radar (Moran et al. 

1998; Martner et al. 2002a). That Millimeter-wave Cloud Radar (MMCR), as well as other 

remote sensing systems, gave ETL experience in building unattended, nearly operational 

 

1

 Note that references in this and all Appendices refer to the Reference section in the main report, and not to the 

chronological Bibliography in this Appendix. 
A-2 

systems. The MMCR now provides 24/7/365 measurements at several DOE CART (Clouds and 

Radiation Testbed) sites worldwide. 

In 1991 and 1999 NOAA/ETL attempted to combine data from a second radar (X-band, 9.3 

GHz or 3.1 cm) with its Ka-band radar data to test a dual-wavelength differential-attenuation 

method for measuring the three-dimensional distribution of liquid water content in clouds. The 

shorter wavelength Ka-band signal is more strongly attenuated than the longer wavelength, and 

in theory the wavelength attenuation difference is directly related to the cloud liquid water 

content. Tests, however, revealed that practical problems (e.g., antenna sidelobes and ground 

clutter in the X-band) and natural cloud conditions (e.g., variations in hydrometeor size 

distribution) disrupt this relationship and make the liquid content estimation dubious under many 

circumstances (Martner et al. 1991, 1993a,b; Vivekanandan et al. 1999). Hence ETL abandoned 

the dual-wavelength approach to icing detection because it appeared to have too many practical 

problems, including the need to precisely match radar sensing volumes while maintaining 

sufficient sensitivity at useful ranges. 

In 1991 NOAA/ETL also began to explore the utility of dual-polarization methods, both 

theoretically and with its Ka-band radar. The theoretical work focused on various observational 

techniques to distinguish the many types of ice particles in clouds, beginning with cirrus 

observations for climate studies. A key advance was the prediction, and subsequent 

confirmation, that the depolarization ratio of different ice and water hydrometeors in clouds 

varies markedly with elevation angle (Matrosov 1991a,b; Matrosov and Kropfli 1993). During a 

series of Winter Icing and Storms Projects (WISP), it was shown that measurements of 

depolarization by various types of ice particles could be used to identify them. In fact, 

hydrometeor evolution could be followed in winter storms (Reinking et al. 1993b, 1995a,b, 

1996a; Matrosov et al. 1995; Matrosov et al. 1996a). Whereas drizzle-sized droplets were 

initially used only for calibration, it was quickly realized that the depolarization measurements 

could be used to distinguish all spherical (liquid) cloud droplets, including SLD, from ice 

crystals (Reinking et al. 1996b-e, 1997a,b). 

Henceforth, ETL’s most important ongoing task for the FAA became the development of a 

dual-polarization Ka-band radar to detect clouds of hazardous SLD, and to distinguish them from 

clouds with non-hazardous ice particles. Supporting theory was improved (Matrosov et al. 

1996a, 2000, 2001; Reinking et al. 1997a, 2002), and in a long series of intensive tests 

culminating with the Mount Washington Icing Sensors Project (MWISP), ETL was able to 

demonstrate a remote-sensing capability for deterministic hydrometeor identification using the 

pattern of depolarization-ratio vs. elevation-angle. These remotely-sensed radar results were 

corroborated by direct detection of cloud particles and precipitation by other means (e.g., aircraft 

and mountain-top in situ sensors). (Matrosov et al. 1996a, 2001; Reinking et al. 1997a,b, 1998, 

2000a-c, 2002). 

This excellent and repeatable agreement between theory, measurement, and independent 

validation thus pointed the way to the design for an operational system of integrated sensors for 

detecting icing conditions aloft, which is GRIDS (Reinking and Kropfli 2000; Reinking et al. 

2001a,b). GRIDS is designed to be an operational system providing 24/7/365 measurements. 
A-3 

A Bibliography of NOAA/ETL Publications Establishing the Foundation of GRIDS, in 

chronological order.2

Westwater, E.R., 1972: Microwave emission from clouds. NOAA TR ERL 219-WPL 18, 43 pp. 

[Avail. from Supt. of Documents, U.S. Gov’t. Printing Office, Washington, DC 20402.] 

Hogg, D.C., F.O. Guiraud, J.B. Snider, M.T. Decker, and E.R. Westwater, 1983: A steerable 

dual-channel microwave radiometer for measurement of water vapor and liquid in the 

troposphere. J. Clim. Appl. Meteorol., 22, 789-806. 

Popa Fotino, I.A., J.A. Schroeder, and M.T. Decker, 1986: Ground-based detection of aircraft 

icing conditions using microwave radiometers. IEEE Trans. Geosci. Remote Sens., GE24, 6, 975-982.

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1991: Progress report on analysis of 

differential attenuation radar data obtained during WISP-91. NOAA Tech. Memo., ERL 

WPL-215, Boulder, CO, 43 pp. 

Matrosov, S. Y., 1991: Theoretical study of radar polarization parameters obtained from cirrus 

clouds. J. Atmos. Sci., 48, 1062-1070. 

Matrosov, S. Y., 1991: Prospects for the measurement of ice cloud particle shape and orientation 

with elliptically polarized radar signal. Radio Sci., 26, 847-856. 

Politovich, M.K., B.B. Stankov, and B.E. Martner, 1992: Use of combined remote sensors for 

determination of aircraft icing altitudes. Preprints, 11th Int’l. Conf. on Clouds & Precip., 

Montreal, Canada, ICCP, 979-982. 

Rasmussen, R.M. Politovich, J. Marwitz, W. Sand, J. McGinley, J. Smart, R. Pielke, S. Rutledge, 

D. Wesley, G. Stossmeister, B. Bernstein, K. Elmore, N. Powell, E. Weswater, B. Stankov, 

and D. Burrows, 1992: Winter Icing and Storms Project (WISP), Bull. Amer. Meteorol. 

Soc., 73, 951-974. 

Matrosov, S.Y., and R.A. Kropfli, 1993: Cirrus cloud studies with elliptically polarized Ka-band 

radar signals: A suggested approach. J. Atmos. and Oceanic Tech., 10, 684-692. 

Stankov, B.B., B.E. Martner, J.A. Schroeder, M.K. Politovich, and J.A. Cole, 1993: Liquid 

water and water vapor profiling in multi-layered clouds using combined remote sensors. 

Paper AIAA 93-0395, Reprints, 31st Aerospace Sciences Mtg., Reno, NV, AIAA, 8 pp. 

Martner, B.E., and R.A. Kropfli, 1993: Observations of multi-layered clouds using K-band 

radar. Paper AIAA 93-0394, Reprints, 31st Aerospace Sciences Mtg., Reno, AIAA, 8 pp. 

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1993: Dual-wavelength differential 

attenuation radar measurements of cloud liquid water content. Preprints, 26th Intl Conf. 

on Radar Meteor., AMS, Norman, OK, 596-598. 

Martner, B.E., R.A. Kropfli, L.E. Ash, and J.B. Snider, 1993: Cloud liquid water content 

measurement tests using dual-wavelength radar. NOAA Tech. Memo. ERL ETL-235, 

NOAA Environmental Research Laboratories, Boulder, CO, 47 pp. 

 

2

 Some of these publications are listed only in this bibliography and are not referenced elsewhere in the text of this 

report. 
A-4 

Reinking, R.F. B.W. Orr, B. Stankov, and C. Davis, 1993: NOAA Ka-band cloud-sensing radar 

measurements from WISPIT. Preprints, 5th Int’l. Conf. on Aviation Weather Systems, 2-6 

August 1993, Vienna, VA. Amer. Meteor. Soc., Boston, MA, 130-134. 

Reinking, R.F., R.Caiazza., R.A. Kropfli, B.W. Orr, B.E. Martner, T.A. Niziol, G.P. Byrd, R.S. 

Penc, R.J. Zamora, J.B. Snider, R.J. Ballentine, A.J. Stamm, C.D. Bedford, P. Joe, and A.J. 

Koscielny, 1993. The Lake Ontario Winter Storms (LOWS) Project. Cover article, Bull. 

Amer. Meteor. Soc., 74, 1828-1829. 

Frisch, A.S., C.W. Fairall, and J.B. Snider, 1995: Measurement of cloud and drizzle parameters 

in ASTEX with Ka-band Doppler radar and a microwave radiometer. J. Atmos. Sci., 52, 

2788-2799. 

Orr, B.W., and B.E. Martner, 1995: Detection of weakly precipitating winter clouds by a NOAA 

404 MHz wind profiler. J. Atmos. Oceanic Tech., 13, 570-580. 

Politovich, M.K., B.B. Stankov, and B.E. Martner, 1995: Determination of liquid water altitudes 

using combined remote sensors. J. Appl. Meteor., 35, 2060-2075. 

Stankov, B. B., B. E. Martner, and M. K. Politovich, 1995: Moisture profiling of the cloudy 

winter atmosphere using combined remote sensors. J. Atmos. Oceanic Tech., 12, 488-510. 

Kropfli, R.A., S.Y. Matrosov, T. Uttal, B.W.Orr, A.S. Frisch, K.A. Clark, B.W.Bartram, 

R.F.Reinking, J.B. Snider, and B.E. Martner, 1995: Cloud physics studies with 8 mm 

wavelength radar. Special issue on cloud microphysics and applications to global climate 

change, Atmospheric Research, 35, 299-313. 

Gibson, J.S., and B.E. Martner, 1995: Interactive auxiliary real-time data system for 

NOAA/ETL Doppler radars. Preprints, 11th Intl. Conf. on Interactive Info. Processing 

Systems for Meteor., Ocean., and Hydrology, Dallas, AMS, 304-309. 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, and B.W. Bartram, 1995: Identification of ice 

hydrometeor types with elliptical polarization radar measurements. Preprints, 27th 

Conference on Radar Meteorology, 9-13 October 1995, Vail, CO. Amer. Meteor. Soc., 

Boston, MA, 539-541. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, R.A. Kropfli, and B.W. Bartram, 1995: 

Hydrometeor identification with millimeter-wave dual-polarization radar. Conf. on Cloud 

Physics, 15-20 January 1995, Dallas, Texas. Amer. Meteor. Soc., Boston, MA, 20-25. 

Reinking, R.F., S.Y. Matrosov, R.J. Bruintjes, B.E. Martner, and R.A. Kropfli, 1995: Further 

comparison of experimental and theoretical radar polarization signatures due to ice 

hydrometeor shape. Preprints, 27th Conference on Radar Meteorology, 9-13 October 

1995, Vail, CO. American Meteorological Society, Boston, MA, 47-49. 

Kropfli, R.A., and R.D. Kelly, 1996: Meteorological research applications of mm-wave radar. 

Meteorol. and Atmos. Phys., 59, 105-121. 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, and B.W. Bartram, 1996: Estimation of 

hydrometeor shapes and types from radar polarization measurements. J. Atmos. Oceanic 

Tech., 13, 85-96. 

Reinking, R.F., S.Y. Matrosov, and R.T. Bruintjes, 1996: Ice and water hydrometeor 

identification with elliptical polarization radar: Applications to precipitation enhancement. 
A-5 

Preprints, 13th Conf. on Planned & Inadvertent Weather Modification, 28 January-2 

February, 1996, Atlanta, GA. Amer. Meteor. Soc., Boston, MA, 172-179. 

Reinking, R.F. S.Y. Matrosov, and R.T. Bruintjes, 1996: Hydrometeor identification with 

elliptical polarization radar: Applications for glaciogenic cloud seeding. Cover article, J. 

Wea. Modif., 28, 6-18. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner and R.A. Kropfli, 1996: Differentiation of freezing 

drizzle from ice hydrometeors and rain with polarization radar. Pro., FAA International 

Conference on Aircraft Inflight Icing. 6-8 May 1996, Springfield, VA, II, 331-338. 

[Available from NTIS, Springfield, VA 22161. DOT/FAA/AR-96/81]. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner and R.A. Kropfli, 1996: Differentiation of freezing 

drizzle from ice hydrometeors with polarization radar. Pro., 12th International Conference 

on Clouds and Precipitation. 19-23 August 1996, Zurich, Switzerland. International 

Commission on Clouds and Precipitation (ICCP); I, 446-449. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, R.A. Kropfli, and B.W. Bartram, 1996: 

Hydrometeor identification with millimeter-wave dual-polarization radar. Proc. ETL/CSU 

Cloud-related Process Modeling and Measurement Workshop, Boulder, Colorado, 23-25 

October 1995. Environmental Technology Laboratory, U.S. Dept. of Commerce, 209-214. 

Reinking, R.F., S.Y. Matrosov, R.J. Bruintjes, B.E. Martner, and R.A. Kropfli, 1996: Further 

comparison of experimental and theoretical radar polarization signatures due to ice 

hydrometeor shape. Proc. ETL/CSU Cloud-related Process Modeling and Measurement 

Workshop, Boulder, Colorado, 23-25 October 1995. Environmental Technology 

Laboratory, U.S. Dept. of Commerce, 215-218. 

Reinking, R.F., S.Y. Matrosov, R.T. Bruintjes, and B.E. Martner, 1997: Identification of 

hydrometeors with elliptical and linear polarization Ka-band radar. J. Appl. Meteor., 36, 

322-339. 

Reinking, R.F., S.Y. Matrosov, B.E. Martner, and R.A. Kropfli, 1997: Differentiation of freezing 

drizzle from ice hydrometeors and freezing rain with dual-polarization radar. J. Aircraft, 

34, 778-784. 

Reinking, R.F., 1997: Hydrometeor and dispersion measurements with 3-cm and 8-mm dualpolarization radar. Pro., WMO Workshop on Cloud Properties for Forecasts of Weather 

and Climate. 23-27 June 1997. Mexico City, Mexico. WMO TD No. 852, 255-269. 

Campbell, W.C. and J. Gibson, 1997: A programmable real-time data processing and display 

system for the NOAA/ETL Doppler radars. Proceedings of the 28th Conference on Radar 

Meteorology, Austin TX, American Meteorology Society, pp 178-179. 

Reinking, R.F., 1998: Detection of ice hydrometeors and freezing drizzle with Ka-band dualpolarization radar. Conf. on Cloud Physics. 17-21 August 1998, Everett, Washington. 

Amer. Met. Soc., Boston. pp 249-252. 

Klimowski, B.A., R. Becker, E.A. Betterton, R. Bruintjes, T. L. Clark, W.D. Hall, B.W. Orr, R. 

A. Kropfli, P. Piironen, R.F. Reinking, D. Sundie, and T. Uttal, 1998: The 1995 Arizona 

Program: Toward a better understanding of winter storm precipitation development in 

mountainous terrain. Bull. Amer. Meteor. Soc., 79, 799-813. 
A-6 

Matrosov, S.Y., A.J. Heymsfield, R.A. Kropfli, B.E. Martner, R.F. Reinking, J.B. Snider, 

P.Piironen, and E.W. Eloranta, 1998: Comparisons of ice cloud parameters obtained by 

combined remote sensor retrievals and direct methods. J. Atmos. Oceanic Tech., 15, 184-

196. 

Kropfli, R.A., B.E. Martner, S.Y. Matorsov, R.F. Reinking, A.S. Frisch, and T. Uttal, 1998: 

Radar and radiometer measurements of the physical parameters of clouds and precipitation 

at the NOAA Environmental Technology Laboratory. Proc. Conf. on Battlespace 

Atmospheric and Cloud Impacts on Military Operations (BACIMO). Hanscom AFB, MA, 

1-3 Dec 1998, 125-132. 

Moran, K.P., B.E. Martner, M.J. Post, R.A. Kropfli, D.C. Welsh, and K.B. Widener, 1998: An 

unattended cloud-profiling radar for use in climate research. Bull. Amer. Meteor. Soc., 79, 

443- 455. 

Reinking, R.F., S.Y. Matrosov, B. E. Martner, B.W. Orr, R.A. Kropfli, and B.W. Bartram, 1999: 

Slant-linear polarization applied to detection of supercooled drizzle. Preprints, 29th Conf. 

on Radar Meteorology, Montreal, Canada, 285-288. 

Reinking, R.F., 1999: Influence of ice particles on remote detection and assessment of 

atmospheric supercooled liquid water. Proceedings, FAA Specialists’ Workshop on 

Mixed-phase and Glaciated Icing Conditions, 2-3 December 1998, Atlantic City, NJ, 

Editors, James T. Riley and Rosemarie McDowall. FAA Technical Center, Atlantic City, 

pp. 303-304. 

Vivekanandan, J., B.E. Martner, M.K. Politovich, and G. Zhang, 1999: Aircraft icing detection 

using dual-wavelength radar observations. IEEE Trans. Geosci. & Remote Sensing, 2325-

2334. 

Frisch, A.S., B.E. Martner, I. Djalalova, and M.R. Poellot, 2000: Comparison of radar/radiometer 

retrievals of stratus cloud liquid water content profiles with in-situ measurements by 

aircraft. J. Geophys. Res., 105, 15361-15364.

Moran, K.P., B.E. Martner, and H.A. Winston, 2000: Dual-polarization and processor efficiency 

enhancements of the DOE’s millimeter-wavelength cloud radars (MMCR). Reprints, 

TECO-2000, WMO Tech. Conf. on Meteor. & Environ. Instruments and Methods of 

Observation, Beijing, China, 4 pp. 

Reinking, Roger F., Jack B. Snider, and Janice L. Coen, 2000: Influences of storm-embedded 

orographic gravity waves on cloud liquid water and precipitation. J. Appl. Meteor., 39,

733-759. 

Ryerson, Charles C., Marica K. Politovich, Kenneth L. Rancourt, George G. Koenig, Roger F. 

Reinking, and Dean R. Miller, 2000: Mt. Washington Icing Sensors Project: Conduct and 

Preliminary Results. Paper # AAIA-2000-0488, Preprints, 38th AAIA Aerospace Sciences 

Meeting and Exhibit, 10-13 January 1999, Reno, Nevada, 10 pp. 

Reinking, R.F., B.W. Orr, L. R. Bissonnette, G. Roy, and A. S. Frisch and S. Y. Matrosov, 2000: 

Remote sensing of cloud droplets during MWISP. Proceedings, Intn’l. Geoscience and 

Remote Sensing Symposium (IGARSS 2000). Honolulu, HI, 24-28 July 2000. Pp 187-189. 

(Available from customer.services@ieee.org). 
A-7 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, B.E. Martner, and B.W. Bartram, 2000: Inferring 

hydrometeor shapes using different polarization states of radar signals. Proceedings Intn’l. 

Geoscience and Remote Sensing Symposium (IGARSS 2000). Honolulu, HI, 24-28 July 

2000. Pp. 1577-1579. (Available from customer.services@ieee.org.) 

Reinking, R.F., S.Y. Matrosov, B.W. Bartram, and R.A. Kropfli, 2000: Evaluation of a slant-45-

quasi-linear polarization state for distinguishing among drizzle droplets and quasi-spherical 

ice particles. Preprints, International Conference on Cloud Physics, Reno, NV, 14-18 Aug 

2000. Pp. 243-246. (Avail. from Desert Research Inst., 2215 Raggio Parkway, Reno, NV.) 

Reinking, R.F., S.Y. Matrosov, C.C. Ryerson, R.A. Kropfli, and B.W. Bartram, 2000: Verified 

detection of supercooled large droplets with dual-polarized, millimeter-wave radar. Ninth 

Conf. on Aviation, Range, and Aerospace Meteorology, 11-15 Sept. 2000, Orlando Fla. 

Amer. Meteor. Soc., Boston. 537-542. 

Reinking, R.F., and R.A. Kropfli, 2000: Breakthroughs in radar polarization technology and 

related studies. Radar-based Icing Diagnosis Techniques, In-flight Icing PDT Technical 

Direction FY2000 Year-end Task Progress Report to the FAA, Deliverable 00.4.3.E1. 

NOAA Environmental Technology Laboratory. 16 pp. 

Matrosov, S.Y., R.F. Reinking, R.A. Kropfli, B.E. Martner, and B.W. Bartram, 2001: On the use 

of radar depolarization ratios for estimating shapes of ice hydrometeors in winter clouds. 

J. Appl. Meteorol., 40, 479-490. 

NOAA ETL Radar Meteorology and Oceanography Division, 2001: Preliminary Design, 

Ground-based Remote Icing Detection System (GRIDS). Report to the FAA Aviation 

Weather Research Program, In-Flight Icing PDT #4, Task 01.4.3.3, FAA Icing Remote 

Sensing Testbed, Deliverable 01.4.3.1.E3. 65 pp + appendices.

Reinking, R.F., R.A. Kropfli, S.Y. Matrosov, W.C. Campbell, M.J. Post, D.A. Hazen, J.S. 

Gibson, K.P. Moran, and B.E. Martner, 2001: Concept and Design of a Pilot 

Demonstration Ground-based Remote Icing Detection System. Proceeding 30th

International Conference on Radar Meteorology, 19-24 July, Munich. Amer. Meteor. Soc., 

Boston, 199-201. 

Reinking, R.F., R.A. Kropfli, S.Y. Matrosov, W.C. Campbell, M.J. Post, D.A. Hazen, J.S. 

Gibson, K.P. Moran, B.E. Martner, and T. Schneider, 2001: Concept and Design of a Pilot 

Demonstration Ground-based Remote Icing Detection System. Proc. Conf. on 

Battlespace Atmospheric and Cloud impacts on military operations (BACIMO). July, Ft. 

Collins, CO. 

Reinking, R.F., S.Y. Matrosov, B.W. Bartram, and R.A. Kropfli, 2002: Evaluation of a 45Ε-slant 

quasi-linear radar polarization state for distinguishing drizzle droplets, pristine ice crystals, 

and less regular ice particles. Journal of Atmospheric and Oceanic Technology, 19, 296-

321. 

Martner, B.E., B.W. Bartram, J.S. Gibson, W.C. Campbell, R.F. Reinking, and S.Y. Matrosov, 

2002: An overview of NOAA/ETL’s scanning Ka-band cloud radar. 16th Conf. on 

Hydrology. Jan 2002, Orlando, Fla. Amer. Meteor. Soc. 
A-8 

Martner, B.E., D.A. Hazen, K.P. Moran, T. Uttal, M.J. Post, and W.B. Madsen, 2002: 

NOAAETL’s vertical-profiling cloud radar and radiometer package. 6th Symp. on 

Integrated Observing Systems, Jan 2002, Orlando, FL, Amer. Meteor. Soc. 

Gibson, J.S., W.C. Campbell, B.E. Martner, 2004: RADS: A radar acquisition and display 

system for research radars. NOAA Tech. Report. OAR ETL-69, NOAA Office of Oceanic 

and Atmospheric Research, Boulder, CO, 56 pp. 

Reinking, R. F., and Sergey Y. Matrosov, 2004: Fall attitudes of pristine snow crystals estimated 

from radar depolarization measurements. Proceedings, 14th International Conference on 

Clouds and Precipitation, 18-23 July 2004, Bologna, Italy. II: 912-915 (Available from 

Inst. Atmos. Sci. and Climate (ISAC), National Research Council, Via Gobetti 101, 40129 

Bologna, Italy). 

Schneider, T.L., S.Y. Matrosov, M.D. Shupe, B.W. Bartram, W.C. Campbell, J.S. Gibson, D.A. 

Hazen, and R.F. Reinking, 2004: An overview of ground-based remote sensing during 

AIRS-2 and WISP-04, using the NOAA GRIDS system. 11th Conf. on Aviation, Range 

and Aerospace Meteorology, Hyannis MA, 11-14 October. Amer. Meteorol. Soc., Boston. 

Available on CD from the AMS. 

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and CIP. 11th Conf. on Aviation, Range and Aerospace Meteorology, Hyannis MA, 11-14 

October. Amer. Meteorol. Soc., Boston. Available on CD from the AMS. 

Schneider, Timothy L., Ben C. Bernstein, and Roger F. Reinking, 2005: Forecasting and 

Ground-based Remote Icing-Detection System (GRIDS) documentation of the transition 

from a glaciated environment to an SLD icing environment. 43rd Aerospace Sciences 

Meeting & Exhibit, 10-13 January 2005, Reno, NV. American Institute of Aeronautics 

and Astronautics, Washington, D.C.. Paper AIAA 2005-0257, 7 pp. 

Matrosov, S.Y., R.F. Reinking, and I.V. Djalalova, 2005: Inferring fall attitudes of pristine 

dendritic crystals from polarimetric radar data. J. Atmos. Sci., 62, 241-250. 

Westwater, E.R., S. Crewell, and C. Matzler, 2005: Surface-based microwave and millimeter 

wave radiometric remote sensing of the troposphere: a tutorial. IEEE Geosci. and Remote 

Sensing Newsletter, Issue 134, 16-33. 

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microwave and millimeter wave radiometeric remote sensing of the troposphere. Proc. 

Societa Italiana di Elettromagnetisomo (in press).

Appendix B 

B-1

Enhancements to Core Icing Algorithm for GRIDS

1. INTRODUCTION 

The core icing algorithm, as described in the main text, has been tested and will provide an 

effective starting point for icing detection. However we can already envision enhancements to it 

that will improve its performance. Most of these enhancements require only software 

improvements, but a few do extend the hardware capability of the radar by adding elevation 

scanning. As with any new system, operational experience will surely lead to further 

enhancements not contemplated here. 

2. LW ENHANCEMENT 

The value of the path-integrated liquid water (LW) sensed by the microwave radiometer can 

be distributed throughout those cloud layers that have liquid water, to estimate LWC (g m-3) in 

each liquid layer and hence the potential severity of the icing hazard. LW of 1 mm equates to an 

average LWC of 1 g m-3 in a cloud 1 km deep. In FAR Appendix C, the minimum LWC 

threshold for moderate icing is at the point (LWC, De, T) = (0.06 g m-3, 40 μm, 20o C), where De

is the droplet size. 

Here is an outline of how it will work in GRIDS: 

We assign liquid only to clouds for which Ze > -23 dBZ, and assume that any part of a cloud 

with a lower reflectivity has minimal LWC. 

(a) If Condition Red (Table 2, Sec. 3.3) is met over the entire cloud path: Determine this 

path length from radar measurements and normalize the liquid water path (LWP) to it. If 

LWC < 0.06 g m-3, the potential icing hazard is minimal. If 0.06 g m-3 < LWC < 0.1 g m-3 light 

to moderate icing is possible. If LWC > 0.1 g m-3, severe icing is possible. These “break point” 

values are the most conservative (smallest) within FAR Appendix C envelopes. Experience with 

GRIDS may show that the break point values should be adjusted. 

(b) If Condition Red is met over only part of the cloud where Ze > -23 dBZ, and the rest of 

the cloud is Condition Yellow or warm (Condition Green), two options are available: 

(1) Allocate all liquid to that depth of the cloud where Condition Red is met. This is the 

most conservative approach, yielding maximum value of LWC and likely overestimating 

the potential icing severity. This approach excludes allocating any liquid to additional 

parts of clouds that may be mixed phase (Condition Yellow) or to any warm cloud 

(Condition Green). 

(2) Assign equal LWC to liquid (or potentially liquid) cloud paths of all three 

conditions. This is the less conservative approach that may underestimate the potential 

icing hazard. 

(c) If Condition Yellow is met and there is no cloud along the path rated Condition Red: 

Assign LWC by either of the two options in (b) above. 
B-2

The table below classifies potential icing hazard according to LWC. A Condition Red 

carries more weight than a Condition Yellow with the same LWC, because the ice particles in 

the Condition Yellow will tend to consume the liquid by vapor deposition or riming. 

Table 1. Icing hazard rating and LWC. 

Condition LWC (g m-3) Hazard Rating 

Red LWC ≥ 0.1 Red-1 

 0.06 ≤ LWC < 0.1 Red-2 

 LWC < 0.06 Red-3 

Yellow LWC ≥ 0.1 Yellow-1 

 0.06 ≤ LWC < 0.1 Yellow-2 

 LWC < 0.06 Yellow-3 

Green LWC (any value) Green 

As noted in Section 3.3.2, it may be possible in the near future to estimate the LW profile 

with reasonable accuracy from continuous radiometric and radar measurements, to which the 

above classifications could be applied. 

3. TEMPERATURE ENHANCEMENTS 

Several measures of the cloud temperatures and the altitude of the freezing/melting level can 

be provided by GRIDS. The RUC model temperature profile is central to the GRIDS algorithm, 

but like all other measures is subject to some error, particularly at times quite apart from the 

12-hr radiosondes that anchor the RUC forecasts. Therefore, the GRIDS estimates of cloud 

temperature and the altitudes of hazardous, supercooled liquid would best be served by 

establishing a hierarchy of priorities and cross-checks in applying radiometric profiling 

measurements, the radiometric layer-mean temperature estimate, the bright band measurements, 

and the RUC. Additional enhancements using icing temperature statistics and employing 

measures of instability that creates SLW can readily be introduced. 

3.1 Radiometric Temperature Profiling Enhancements 

The 30 and 90 GHz dual channel radiometric data can be manipulated to estimate a layermean temperature of the cloud LW. GRIDS software is readily adaptable to include this 

estimate. The GRIDS design also includes a third, ~55GHz, radiometer channel, which provides 

estimates of the temperature profile. Since it is part of the hardware design, this option has been 

discussed in more detail in the main body of the text. As noted there, the temperature profiling 

radiometer is limited in measuring detail and is best in the lowest few kilometers of the 

troposphere. It generally overestimates temperature in the upper reaches of inversions, and 

underestimates in the lower regions, but it can provide a good smoothed temperature profile, 

continuously with temporal resolution of seconds, to verify and compare to the RUC output. 

This cross-check on the RUC will be particularly important in situations with frontal activity 

where the 1-hour resolution and time-interpolation of the RUC may become too coarse. These 

radiometric temperature data are not included in the GRIDS core algorithm, but its inclusion as 

an enhancement, is recommended. 
B-3

3.2 Temperature Instability Enhancement 

The tropospheric profile of dew point temperature as well as the profile of temperature can 

be downloaded from the RUC model output. Therefore, by applying an appropriate (in)stability 

index, such as either the Showalter or Lifted Index, the potential for cloudy convection can be 

estimated quantitatively. This can be integrated with the core algorithm decision points to 

additionally estimate the potential for enhanced condensation and LW by convection, which if 

supercooled can increase the icing potential. Particularly, the degree of correlation between the 

instability and enhanced LW should be established through field tests. The instability indicator 

will be valid only if increased production of LW is observed, and only then can the warning level 

be upgraded. The simplest approach would be to make a yes or no determination as to whether 

any part of the cloud depth is unstable. The degree of instability could add further detail. 

3.3 Temperature Classification Enhancement 

Further refinement of the potential icing hazard is possible via temperature classification. A 

statistical study (Schultz and Politovich 1992) estimated that approximately 90% of icing 

incidents occur in the temperature range of 0o C to -20 o C, and 70% occur when 

-15o C < T < -2o C. Icing potential increases with temperature because warmer air can hold more 

vapor and thus produce more condensed liquid. A temperature classification such that T ≤ 20o C 

means minimal risk, -20o C < T ≤ -10o C means moderate risk, -10o C < T < 0o C means highest 

risk, and T > 0o C means no risk could be applied alone, or in combination with the LWC 

stratification. In mixed phase clouds, which are readily detected by the radiometer-radar 

combination, allowance can be made for consumption of LW by the ice crystals, which is 

temperature dependent. This consumption is maximized, and the icing potential is diminished

primarily near -15o C and secondarily near -5o C. Also, since temperature and LWC are not truly 

independent, applying an additional temperature classification becomes somewhat redundant and 

adds complication. Still, from each point (LWC, T) in the cloud profile, it may be possible to 

estimate not only the icing severity, but also the droplet size (De) that is contributing to the 

hazard. After a sufficient set of GRIDS data are acquired and analyzed, it will be possible to 

study such an enhancement. 

3.4 Bright band (melting level) enhancement 

Obviously, supercooled cloud liquid can occur only at altitudes where T < 0°C. These 

altitudes will begin slightly above the melting level if one occurs. The melting level is detected 

with radars as the bright band. Freezing drizzle and rain can occur at the surface when falling 

snowflakes melt, and the droplets they create fall into colder, sub-cooled air nearer the surface. 

The first indicator of falling drizzle or rain is the appearance of a bright band, the signature of 

which is much more defined in the depolarization measurement than the reflectivity 

measurement. It is lucidly depicted by the GRIDS radar as a line of extremely high 

depolarization (DR approaching 0 dB, the level for complete depolarization), clearly separated 

from much lower DRs immediately above and below the melting level. GRIDS will also show 

the minimal signature value of DR in freezing drizzle, but may show a DR that decreases with 

range in freezing rain due to non-Rayleigh scattering (Reinking et al. 1997b). Any effects of rain 

attenuation on reflectivity at Ka band will be much reduced at the 40o antenna elevation from 

that measured at much lower elevations commonly used with scanning radars. Also, a Ze > -15 

dBZ in all range gates between the surface and the bright band will show that this hazardous 
B-4

precipitation is reaching the surface. The GRIDS surface temperature and/or the ingested 

temperature profile will indicate if the necessary supercooling is occurring below the melting 

level. Algorithms for detecting freezing drizzle and rain by measuring the vertical DR gradient 

to isolate the bright band have been developed for other applications and can be adapted to 

GRIDS.

4. VERTICALLY-POINTING ENHANCEMENT (DEPOLARIZATION AND 

VERTICAL MOTION MEASUREMENTS) 

In mixed-phase rather than liquid-only clouds, cloud physics principles dictate that ice 

particles will rapidly consume liquid droplets, so the LW component in mixed-phase clouds can 

present a lasting icing hazard only if the dynamics support continued condensation that outpaces 

the consumption by ice. The GRIDS radar includes Doppler measurements, so estimates of the 

beam-radial velocity, Ve, of reflecting hydrometeors and the variance of Ve are routinely 

measured. Without modification to GRIDS, the velocity variance measured in cloud along the 

40° slant-path can be taken directly as an estimate of in-cloud turbulence. The degree of 

turbulence can in turn serve as an indicator of heightened potential for icing, because shear zones 

and convection tend to produce relatively large and dangerous SLDs. Additional gains in icing 

hazard detection can be made by modifying the radar to alternate between pointing to the 40° 

elevation and then to the vertical. 

The GRIDS radar is designed with options to replace pulse-pair processing with spectral 

processing, and to add a zenith-pointing capability. Pointing to zenith and 40o elevation in 

alternating 5 min periods would provide superior ice particle identification in DR, as well as 

measurements of Doppler vertical velocity and vertical velocity variance. The pointing angle of 

the microwave radiometer would alternate in synchronization with that of the radar. Spectral 

processing could add sensitivity to the radar, and if implemented with the zenith-pointing option, 

would provide spectral measurements of the vertical velocity (Schneider et al. 2004, 2005). 

Formulations of specific algorithm enhancements from these additional measurements offer 

considerable promise. 

The identification of the different ice particles in itself can add another level of confidence 

to detection of an icing condition, especially in those clouds of mixed-phase. Ice crystal families 

can be identified and differentiated from droplets with a high level of confidence in the 40o 

elevation DR measurement alone. However, the specific type of ice particle and changes in 

particle type are best established by measuring DR as a function of a full range of elevation 

angles, or at a minimum of two elevation angles including zenith. This has been thoroughly 

demonstrated (see e.g., Matrosov et al. 2001 for the theory and Reinking et al. 2002 for the field 

demonstration). Ice types are more specific indicators of the microphysical processes that are 

active in a cloud, and hence of the likely rate of consumption of hazardous liquid by ice. For 

example, the presence of pristine ice crystals indicates minimal riming of the crystals by cloud 

droplet collection, and thus a minimal icing hazard. However, the presence of graupel is 

normally indicative of liquid-producing convection; it shows that considerable liquid-consuming 

riming has occurred, and warns that liquid build-up may occur in successive convective 

elements. 

Doppler velocity parameters can be used to estimate both vertical air motion and the falling 

speed of hydrometeors when the radar is pointed to zenith. Mixed-phase clouds can produce 
B-5

droplets of sufficient size and LWC to be an icing hazard only if upward air motion is sufficient 

to condense liquid faster than the ice can consume it. Strong upward air motion can be liquidproducing when it exceeds the terminal velocity of ice particles (hail excluded). Vertical 

velocity spectra might help decide if the cloud is of single or mixed phase, and they can be used 

to estimate the effective size of any droplets from differences in ice particle and cloud droplet fall 

speed (Zawadski et al., 2000). 

The GRIDS radar can operate in a vertically-pointing, time series mode that provides 

measurements of the full Doppler spectra, which is the distribution of returned power as a 

function of the velocity of the targets within a given range gate. In effect, the Doppler spectrum 

is a convolution of hydrometeor fall speeds and air turbulence combined with mean air motions. 

Under minimal turbulence, Doppler spectra from single-phase (all ice or all liquid) clouds are 

relatively narrow. The presence of turbulence, multiple phases of particle habits, and /or vertical 

wind shear can lead to relatively broad and possibly multi-modal Doppler spectra. These 

Doppler spectra features may provide information that is useful for identifying and quantifying 

potential aircraft icing conditions. A demonstration of this is as follows (from Schneider et al. 

2004). Figure 1 shows three Doppler moments (reflectivity, velocity, spectral width) measured 

with the NOAA-K (GRIDS demonstration) radar during AIRSII. A spiral descent by NASA 

research aircraft confirmed the supercooled liquid near the top and the additional complex nature 

of this cloud. 

Figure 1. Doppler moments of reflectivity, velocity, and spectral width measured during AIRSII. 

Twin Otter spiral descent 

Twin Otter: 

Icing near cloud top 

LWC ~0.5g/m3 

NASA radiometer: 

LWP ~210-280 g/m2 

17:00 Z sounding: 

T<0 C above 2.2 km 
B-6

A spectrograph that depicts the evolution of the Doppler spectra with height during this case 

is shown in Figure 2. Horizontal slices of this contour plot represent individual Doppler Spectra. 

Both above and below the melting level (~2.2 km), bi-modal spectra suggest that multiple phases 

and crystal growth habits may have been present. Above 2.2 km, bi-modal spectra (e.g., inset 

panel) may indicate the presence of supercooled liquid droplets at negligible fall speeds and ice 

particles at fall speeds near 0.5 m s-1. At 2.0 and 1.1 km there are indications of liquid cloud 

layers near 0 m s-1 that produce drizzle at higher fall speeds. The DR measurement with GRIDS 

combined with the path LW from the microwave radiometer will readily show the all-liquid 

versus mixed phase clouds, but the altitudes of the LW is not determined. This type of spectral 

information, combined with temperature profiles, may provide a means for identifying the 

distribution of mixed-phase cloud regions with supercooled liquid. In some cases, the 

microphysical properties of both liquid and ice components can be derived from these spectra. 

Figure 2. Spectrograph of the evolution of Doppler velocity spectra with altitude. 

5. RAIN RECOGNITION ENHANCEMENTS 

The recognition of degradation of signals caused by rainfall, and definition of appropriate 

decision-path alterations are important parts of the GRIDS algorithm. This is mentioned last in 

this list of specific potential enhancements only because some of the possible approaches to rainrecognition depend on some of the aforementioned enhancements. 

As noted before, rain could degrade the signal received by the microwave radiometers. The 

core icing hazards algorithm does not incorporate rainfall directly because the commercially 

available microwave radiometers integrate a blower system to dry a microwave penetrating 

window that protects the antenna. However, experiments by NASA with these drying devices 

Melting 

Liquid 

MixedDrizzle

Ice

Liquid 

Ice 
B-7

during the 2nd Alliance Icing Research Study (AIRSII, 2003) indicate that unreasonably high 

values of LW are obtained during significant rain. Drizzle is unlikely to induce false readings 

(Reinking et al. 2000a), but at this writing a rain-rate threshold above which the dryers fail has 

not yet been established. 

To address this issue, one reasonably direct solution to this problem is to use the surface 

rain-rate sensor incorporated with GRIDS and apply a sensible, conservative threshold to the 

measurement. Thus, for example, if RR > N mm/hr (where N is yet to be determined), and other 

algorithm thresholds are met at some altitudes, a yellow warning would be issued for those flight 

levels. This simple rain-no rain decision would indicate that there is uncertainty in the reliability 

of the radiometer measurement, but icing is possible. 

This can be upgraded further by directly sorting reliable from potentially unreliable LW 

measurements by evaluating the values and variance of the radiometer’s LW signature. The 

yellow warning would then be issued only if the rain-rate threshold is met, the other parameters 

meet their criteria for a hazard, and the LW signal is erratic (large variance) with high value 

spikes commonly well in excess of 2 mm. 

Alternatively or additionally, the radar itself is a well-established instrument for detecting 

and estimating rain rate via the standard reflectivity-rain rate (Z-R) relationships. Algorithm 

enhancements that use the radar rather than the rain-rate sensor to identify rain will be only 

slightly more complicated but are certainly achievable and can more readily deal with 

distinguishing snowfall (which will not degrade the radiometer or radar measurements) from 

rainfall, by also using bright-band detection and the depolarization measurements. 

Further use of the radar can be made if the vertically-pointing option is implemented. Then 

the measurement of the vertical velocity within the clouds above can be incorporated to identify 

updrafts that are likely to produce regions of elevated LW vs. benign motion that is nonproductive. This option would be particularly useful to refine the warning of icing potential 

during rain to an estimate that is more quantitative than simply “yellow.” Additionally, or 

alternatively, estimates of vertical motion from the RUC model could be used for the same 

purpose, although they would be filtered by the larger scale they represent. 

The Ka-band radar’s attenuation due to rain should also be noted. It can be expressed as 

Attenuation (dB) = 0.56 * rain rate (mm/hr) * range (km). 

Thus a 5mm/hr rain measured at 3 km range would cause ~8.4 dB attenuation. Due to the 

low altitude of the bright band in (winter) icing conditions, and the high pointing angle of 

GRIDS, the propagation path through rain falling between the bright band and the radar is 

normally quite short, such that only heavy rain will seriously attenuate the radar reflectivity (and 

the radiometer measurements as well) and render the GRIDS icing algorithm unusable. Under 

such circumstances of uncertainly, only a yellow warning can be issued. We further note, 

however, that when heavy rain is being produced by stratiform clouds, the ice processes 

removing the SLW are very active, so these situations are likely not a prime time for icing 

events. Note that attenuation is rarely sufficient to impede the vertical motion and Doppler 

spectra measurements, so algorithms should be adjusted to focus on those. If the clouds are 

severely convective, aircraft would be warned away by on-board radars or NEXRAD, regardless 

of the icing conditions, so that is not a situation that GRIDS needs to address. 
B-8

6. GRIDS-CIP SYNERGY 

The noted enhancements to the GRIDS algorithm begin a list that will add to the power of 

GRIDS. As new developments in instrumentation, measurements, and modeling that could 

support GRIDS come on line, they would logically be examined and incorporated as appropriate. 

Integration of the Current Icing Product (CIP, McDonough and Bernstein 1999, Bernstein and 

Schneider 2004) could be one such strong addition. The CIP integrates several network data 

products to produce mapped estimates of icing and SLD potential on a 0.0-10 scale and icing 

severity on a 0-8 scale. The integrated network products include satellite and NEXRAD data, 

surface observations (ceiling, precipitation,, type), lightning, pilot reports of icing, RUC 

temperature, relative humidity, vertical motion, and SLW profiles. 

Figure 3 contains a schematic of these data ingests and a flow diagram that shows how the 

CIP data are translated into icing potential. The analyses that are part of the GRIDS Operational 

Experience (Section 2.5 of this report) illustrate the potential synergy between real-time 

measurements with GRIDS and application of the CIP. Each is a stand-alone system, but 

together the power for identifying the real icing potential can be greatly enhanced. Comparison 

of the GRIDS and CIP estimates of icing potential would serve to substantiate and refine both 

systems. The GRIDS local area measurements would anchor the CIP estimates, and the CIP 

estimates would extrapolate the GRIDS verification to the larger area. 
B-9

Figure 3. The Current Icing Potential Product (CIP) in its current state of development. 

Appendix C 

C-1 

GRIDS Requirements

1. DESCRIPTION 

The GRIDS (Ground-based Remote Icing Detection System) is an autonomous and reliable 

pilot demonstration radar/radiometer system whose purpose is to detect, from the ground, icing 

conditions that are hazardous to aircraft. To accomplish this, it utilizes a Ka-band radar 

transmitting a circularly polarized signal (the co-polarized signal) and receiving a co-polarized 

and cross-polarized signal simultaneously, using a fixed pointing angle of about 40 degrees. 

Icing conditions are detected using an algorithm that utilizes the depolarization ratio between the 

co-polarized and cross-polarized channels, the absolute reflectivity from the co-polarized 

channel, liquid water information from 23.8 GHz and 31.6 GHz radiometers, local surface 

temperature and humidity, and temperature profiles retrieved from a 55 GHz radiometer and 

from the RUC (Rapid Update Cycle) model, which is obtained via the Internet. GRIDS will run 

continuously while unattended and transmit data over the Internet for archival at ETL. A display 

indicating the possibility of aircraft icing will be served over the Internet and will be accessible 

with common Web browser software. 

Functionally, GRIDS consists of a container system (a seatainer), a radar system, three 

radiometer systems, a surface meteorological system and a data processing system. An Internet 

link is needed to ingest model temperature data and to communicate warnings, data and system 

health information to the outside world. The radar system consists of the radar transmitter, radar 

receiver and antenna. The radiometer systems consist of a dual-channel, liquid/vapor microwave 

radiometer and a 55 GHz temperature-profiling radiometer. The radiometers are mounted in a 

PSR scanhead that retracts into the container for calibration. 

2. CONTAINER AND ENVIRONMENTAL REQUIREMENTS 

2.1 The GRIDS shall be housed in a seatainer of dimensions 8’6” H x 8’ W x 20’ L. 

2.2 The GRIDS shall be capable of being transported by flat bed semi-trailer (min. 30 ft long, 

oversized load due to the antenna). 

2.3 The GRIDS shall operate on single-phase, 240 VAC power, center-tapped, at 60 Hz. 

2.4 The GRIDS shall operate over an ambient temperature range of -20 deg C to +40 deg C. 

3. RADAR REQUIREMENTS 

3.1 The radar shall operate in the Ka band at about 35 GHz. 

3.2 The radar shall transmit a single circular polarization and receive simultaneously that 

polarization (co-polarization) and the orthogonal polarization (cross-polarization). 

3.3 The radar transmitter shall use a TWTA. 

3.4 The radar receiver shall receive simultaneously the co-polar and cross-polar channel, and 

provide these signals at a 60 MHz IF frequency for use in a digital receiver. 
C-2 

3.5 The radar receiver shall provide a 20 MHz reference signal synchronous with the 60 MHz 

IF frequency. 

3.6 The radar shall meet the performance requirements indicated in Table 1. 

3.7 The antenna shall be manually adjustable, in less than one day, from at least 30 to 90 

degrees. The nominal pointing angle is 40.2 degrees. The design shall allow for easy 

upgrade to a motor drive that will slew between 40.0 degrees and 90.0 degrees in less 

than 1 minute. 

3.8 It is a design goal that it will be easy to add system health capability as used in the 

MMCR. 

Table 1. Radar specifications. 

Parameter Min Nominal Max Units Comment 

Transmit frequency 34.5 34.86 35.5 GHz 

Transmit tube lifetime 20000 hours 

Pulse repetition period 50 150 μsecs 

Peak transmit power 1000 watts 

Duty cycle 15% 

Transmit pulse width 0.05 2.0 10 μsecs 

Receiver noise floor -105 -100 dBm 

Antenna diameter 3 meters 

Antenna elevation 30 40.2 90 degrees mechanically adjustable 

Antenna elevation 30 90 degrees scanning option 

Antenna beamwidth 0.2 degrees circular beam 

Antenna cross-polar 

isolation 

30 dB as measured on antenna range 

Antenna gain 57 dB 

4. RADIOMETER SYSTEM REQUIREMENTS 

4.1 The radiometer system shall consist of a dual-channel, liquid/vapor microwave radiometer 

meeting the specifications shown in Table 2 and a temperature-profiling 55 GHz 

radiometer. 

4.2 The radiometer shall be capable of being adjusted to selected fixed elevations, or scanned 

in elevation to match the positioning options for the radar. 

4.3 The microwave radiometers shall perform auto-calibrations at regular intervals. 
C-3 

Table 2. Liquid/vapor radiometer specifications. 

Parameter Min Nominal Max Units Comment 

Frequencies 23.8 

31.6 

 GHz 

Bandwidth 400 MHz 

Antenna beam width 5.7 degrees 

Accuracy 0.5 degrees K 

Operating temperature -20 +50 C 

4.4 The temperature-profiling radiometer system shall provide a profile of temperature 

suitable for use in the icing algorithm. 

4.5 The temperature-profiling radiometer shall have several channels around 55 GHz. 

4.6 The radiometer system shall process the raw radiometer data to produce brightness 

temperatures, integrated liquid water, integrated ice and a temperature profile. 

4.7 The radiometer computer shall transmit the radiometer data to the GRADS via Ethernet. 

5. GRIDS RADAR ACQUISITION AND DATA SYSTEM (RADS) 

REQUIREMENTS 

5.1 GRADS shall support unattended operation by executing operator-defined queues of 

operating parameters, and by allowing remote (via Internet) monitoring of its 

operation. 

5.2 GRADS shall process radar data in real-time in a covariance (pulse-pair) and spectral 

mode, and calculate, as a minimum, the fields shown in Table 3. DC correction shall 

be applied. 

5.3 GRADS shall be capable of performing spectral processing of time series data. 

5.4 GRADS shall be capable of displaying data fields from the covariance and spectral 

processing methods in a time-range (A-scope) format. 

5.5 GRADS shall be capable of saving covariance and spectral data to disk. 

5.6 GRADS shall ingest temperature profile data from external sources via the Internet for the 

purpose of determining the freezing level. The system will be able to obtain 

temperature data accurate to one degree Celsius from ground level to ten km in 

altitude, once per hour for a point within 40 km radius with a vertical resolution 

between 100 and 300 meters. 
C-4 

Table 3. Moment data fields. 

Field From copolar 

channel 

From crosspolar 

channel 

From both 

channels 

Units Purpose 

Velocity Vc Vx m/s research 

Width Wc Wx m2/s2 research 

Correlation (covariance 

mode only) 

Cc Cx None diagnostic 

Intensity (power at 

receiver output) 

Ic Ix dBm diagnostic 

Power received (at 

antenna terminals) 

Pc Px dBm diagnostic 

Reflectivity Zc Zx dBZ algorithm 

Depolarization ratio DR dB algorithm 

5.7 GRADS shall ingest liquid-water and temperature-profiling radiometer data. 

5.8 The data processing system shall run unattended with no local operator intervention 

except for maintenance. 

5.9 GRADS shall integrate radar, radiometer, and temperature data and execute an icing 

detection algorithm in real time. 

5.10 GRADS shall provide an easy-to-interpret time-height display of icing conditions within 

clouds, possibly based on a green-yellow-red color scheme. The current icing 

condition display shall be served on the Internet and be accessible to interested parties 

through a standard Web browser, in near real-time. Displays will be updated every 60 

seconds and will have a maximum delay of no more than three minutes. 

5.11 Moment data shall be sent over the Internet and archived remotely. Because of the 

possibility of Internet outages, data shall be buffered locally until it has been sent. 

Sufficient local storage shall be provided to buffer data for 48 hours without loss of 

data. 

5.12 GRADS shall meet the requirements specified in Table 4. 
C-5 

Table 4. GRADS performance specifications. 

Parameter Min Nominal Max Units Comment 

A/D converter, channels 2 two parallel channels at IF 

A/D sampling rate 80 100 MHz 

A/D bits 14 Bits 

A/D dynamic range 71 dB S/(N + D) 

Pulse repetition period 

(PRP) 

50 1000 μsecs 

Resolution of PRP 1 μsecs 

Number of range gates 4 72 may vary based on other 

parameters 

6. OPTIONS 

6.1 Improved cloud particle information 

6.1.1 Dual angle beam positioning antenna 

This option would allow the radar to alternate between a low pointing angle to a vertically 

pointing configuration, using limit switches, in no longer than one minute. The azimuth will 

be unchanged. This feature would be controlled automatically. The two limits can be 

mechanically changed within the range 30 and 90 degrees. The radiometer antennas would 

move with the radar antenna. 

7. TESTING AND DEMONSTRATION 

7.1 Testing 

The radar shall pass the following tests before being deployed for demonstration: 

7.2 Basic radar function test. 

Transmit power and signal sensitivity will be tested and evaluated. 

7.3 Radar calibration test. 

GRIDS will be operated close to another Ka-band radar. Echo strength from atmospheric 

targets will be compared. 

7.4 Local data archiving test. (with scheduling) 

Typical operating modes will be scheduled and the data will be recorded in the radar for a 

period of at least one week. The data taken and the operation of the radar will be monitored 
C-6 

to insure that the radar has operated continuously with no manual intervention. This test may 

be run concurrently with 7.5. 

7.5 Remote data archiving test. (with scheduling) 

Typical operating modes will be scheduled and the data will be archived remotely for a 

period of at least two weeks. The data taken and the operation of the radar will be monitored 

to insure that the radar has operated continuously with no manual intervention. This test may 

be run concurrently with 7.4. 

8. TYPICAL OPERATING MODES AND SENSITIVITY 

The following table shows the independent radar parameters for typical operating modes. 

Parameters below the double line are independent parameters that may be altered in software. 

Parameters below the triple line are dependent parameters. A dual-channel receiver is assumed. 

Note that the dwell time and vertical range resolution are held constant in the table. 
C-7 

Table 5. GRIDS operating modes 

mode 40 deg slant 40 deg slant vertical vertical 

radar wavelength 8.6 mm 8.6 mm 8.6 mm 8.6 mm 

antenna diameter 3.0 m 3.0 m 3.0 m 3.0 m 

peak transmitter power 1000 watts 1000 watts 1000 watts 1000 watts 

elevation angle 40.2 degrees 40.2 degrees 90 degrees 90 degrees 

pulse repetition period 110 μsecs 110 μsecs 71 μsecs 71 μsecs 

pulse width 1.55 μsecs 1.55 μsecs 1.00 μsecs 1.00 μsecs 

number of time-domain 

averages 

1 1 1 10 

number of FFT points 64 points 256 points 256 points 256 points 

dwell time 60 secs 60 secs 60 secs 60 secs 

range gate spacing 1.55 μsecs 1.55 μsecs 1.00 μsecs 1.00 μsecs 

number of range gates 69 69 67 67 

receiver bandwidth 0.645 MHz 0.645 MHz 1.0 MHz 1.0 MHz 

unambiguous radar range 16.49 km 16.49 km 10.64 km 10.64 km 

minimum height 68 m 68 m 105 m 105 m 

maximum usable height 10.2 km 10.2 km 10.0 km 10.0 km 

maximum unambiguous radial 

velocity 

±19.55 m/s ±19.55 m/s ±30.28 m/s ±3.03 m/s 

maximum unambiguous 

horizontal velocity 

±25.60 m/s ±25.60 m/s NA NA 

time available for one 

spectrum computation 

51 μsecs 204 μsecs 135.6 μsecs 1356 μsecs 

number of spectrum averaged 

per gate per dwell time 

8523 2131 3301 330 

duty cycle 1.41% 1.41% 1.41% 1.41% 

average power 14.1 watts 14.1 watts 14.1 watts 14.1 watts 

range resolution 232.3 meters 232.3 meters 150 meters 150 meters 

height resolution 150.0 meters 150.0 meters 150 meters 150 meters 

radial velocity resolution 0.611 m/s 0.153 m/s 0.237 m/s 2.366 m/s 

Estimated sensitivity at 5 km 

AGL using spectral 

processing 

-69.4 dBZe -66.4 dBZe -73.1 dBZe -78.1 dBZe 

Estimated sensitivity at 6 km 

AGL using spectral 

processing 

-67.8 dBZe -64.8 dBZe -71.5 dBZe -76.5 dBZe 

Estimated sensitivity at 10 km 

AGL using spectral 

processing 

-63.4 dBZe -60.4 dBZe -67.0 dBZe -72.0 dBZe 

Appendix D 

D-1 

GRIDS Block Diagram and Parts List

Table 1 shows the parts referenced in the block diagram and gives details on parts that have 

been specified. COTS refers to commercial-off-the-shelf components. 

Description Manufacturer Model Comment 

Traveling wave tube 

amplifier 

 Specifications req’d 

IF Modulator/Receiver Build in-house 

Coherent Up/Down 

Converter (CUDC) 

 Specifications req’d 

Low-noise amplifiers May be incorporated in 

CUDC 

Circulator assembly 

Orthomode transducer COTS 

Polarizer COTS 

Antenna Specifications req’d 

Oscilloscope Used as “A-scope” 

Linux rack-mountable PC Technoland DIFRS computer 

Frequency synthesizer card Echotek ECSG-1R3ADCPMC Part of DIFRS 

Digital receiver card Interactive Circuits 

& Systems 

ICS-554B-2-MN Part of DIFRS 

Radar Timing Generator card Acromag IP1K110-2412 Part of DIFRS 

Noise diodes 

Circulator drivers 

Pulse controller Vaisala 

Linux rack-mountable PC Technoland RC+ computer 

GPS card Symmetricom BC6371PCI Part of RC+ 

Surface meteorological 

instruments 

 

Windows PC GRIDS-AUX computer 

A/D card Part of GRIDS-AUX 

Digital I/O card Part of GRIDS-AUX 

IEEE-488 card Part of GRIDS-AUX 

Three-channel radiometer ETL GSR or 

Radiometrics 

TP/WVP-3000 

 

Radiometer computer 

Linux rack-mountable PC Technoland RIPS computer 

Ethernet switch generic 

Linux rack-mountable PC Technoland Firewall computer 

Uninterruptible power 

supply 

 

Table 1. Partial parts list. 
D-2 

main IF 20 MHz

IF main, IF 

cross, log main, 

SYNC, SMGT 

Det RF

Det RF

TX, BL, SY, SM XMIT 

level set 

[5] 

TREN, TXPL, BLNK 

RF XMIT

1 Gbit 

Ethernet

RF 

Diode

20 MHz 

cross IF 

main RF

main IF

IF XMIT Circ

Asse

Coherent Up/Down 

Converter 

TWTA 

cross RF

IF Modulator/Receiver 

cross IF

TWT Gate

IEEE-488

RF XMIT

DIFRS – Digital IF Rcvr 

Subsystem 

Linux PC

Freq Synth Card 

DR Card RTG Card 

80 MHz DTRG

Pulse 

TWT Gate

Noise 

Diode

RC+ -- Radar  Control + Linux PC 

GPS 

Surface 

Met 

Ethernet Switch Firewall Computer Linux PC 

Guest 

Computer 

A/

ThreeChannel 

Radiometer 

LNA

O’scope 

GRIDS Block Diagram 

Notes: 1)RF frequency is 34.86 GHz. 1) IF frequency is 60 MHz. 2) Displays & keyboards for computers 

are not shown. 

3) LNAs may be incorporated in CUD. 

RIPS – Remote Integrated 

Product Server 

Linux PC Radiometer Computer 
Appendix E 

E-1 

GRIDS Features and Benefits

Feature Benefit Comment TWTA (traveling wave tube amplifier) transmitter 

Longer lifetime than magnetron tube. Allows for higher transmit power, if necessary. 

Higher power would require a longer ppulse coding would have to be implem

MMCR-based radar design 

Mature design that has performed well in the several deployed MMCRs. 

RADS-based computer design 

RADS was developed by current staff and has performed well in ETL’s research radars. Allows for complete visibility of all data in real time. 

Housed in seatainer Allows for easy, inexpensive shipping. 

Inexpensive, rugged container. Strong enough to serve as a mounting point for the antenna. 

Works especially well for transport by 

Icing data served by remote server 

Allows for many simultaneous accesses to icing data without compromising the performance of GRIDS. 

Data archived remotely 

Eliminates the need for local operators. 

System UPS Protects system from intermittent power 

outages. Allows for complete unattended recovery from a power outage. 
E-2 

Monitoring and notification of system health 

Allows the system to work without constant attention of a technician. 

Firewall computer Protects other computers from computer 

vandals. Simplifies computer security. 

Most security updates need only be imfirewall computer. This enhances the other computer systems. 

Spectral processing Improves performance in conditions of low 

signal. 

Moveable antenna (slant and vertical operation) 

In conjunction with spectral processing, differentiates ice vs. droplet fall velocities to enhance icing algorithm in mixed phase clouds. 

Dual receiver Improves performance in conditions of low 

signal by 3 dB. 

Spares Improves mean time to repair, dramatically so in 

the case of a failure of a radar component 

Some radar components have delivery months. 

Complete system Designed to be close to an operational 

prototype. Would allow systems to be put into production quickly. 
Appendix F 

F-1 

Software Catalog

1. INTRODUCTION 

This Appendix is intended to provide a brief overview of the software that was developed 

for GRIDS. Because the project was terminated before completion, some of the software is 

incomplete. The software is divided into two categories: utility programs and programs that run 

on the Digital IF Receiver Subsystem (DIFRS) computer; and programs that run on the Radar 

Control Plus (RC+) and Remote Integrated Product Server (RIPS) computers. This software was 

written by two different people, and is largely uncoupled, so it is convenient to divide it in this 

way. 

Many of the data structures used in the software are based on legacy formats, and so a brief 

review of these structures is given here. 

1.1 Universal Format 

Universal Format (UF) was developed at a meeting of meteorological radar researchers 

(Barnes 1980) to serve as a common format for recording data from Doppler radars. It was 

based on the current technology at the time, namely 16-bit minicomputers and 9-track magnetic 

tape. This was later extended to recording data on disk through the use of the “4-record-4” 

format. 

1.2 4-Record-4 format 

 4-record-4 format was developed to create a virtual magnetic tape on a single disk file. 

Magnetic tapes have records, end-of-file (EOF) marks, and an end-of-tape (EOT) mark, and this 

needed to be simulated within a disk file. The encoding to do this is called 4-record-4 format, 

because four bytes containing the record length in bytes are written before the record; then the 

record itself is written; and then four bytes containing that same record length are written 

following the record. The record length written does not include the “4” information, and is 

always written in big-endian format. EOF marks are simulated by writing a zero-length record, 

which is eight bytes of zeroes. The EOT mark is represented by two EOF marks in a row, or 16 

bytes of zeroes. 

1.3 Extended Format 

UF uses 16-bit integer data, and so must be extended for floating-point data. In the 

Extended Format (EF), the first two bytes contain ASCII ‘EF’ instead of ‘UF.’ And the data 

(non-header) fields contain 32-bit IEEE floating-point data. 

1.4 Radar Parameters 

The Radar Parameters, which contains all the parameters necessary to specify the operation 

of the radar, are represented in a Radar_Params structure consisting of several data types. This is 

defined in Gibson et al 2004. 
F-2 

2. DIFRS AND UTILITY SOFTWARE 

2.1 Introduction 

Except for drex.cpp, all these programs are utility programs that may be run on any 

machine. drex.cpp, however, is designed to work with the Digital Receiver card to ingest data, 

and so will only run on the DIFRS computer. At project termination, all of these programs were 

functional, but drex.cpp had some limitations as discussed below. What is missing from this set 

of programs is one that runs both the Digital Receiver and the Radar Timing Generator, and 

sends the data via Ethernet to the RC+ computer. To complete the functionality of DIFRS, 

drex.cpp can be used as a basis for creating this program. 

All the programs, libraries and classes in this section are written in C or C++, and run with 

the Linux operating system. 

2.2 Standalone Programs 

2.2.1 diff_hdr_dump.cpp 

diff_hdr_dump is a program that reads radar data records recorded with “extended format” 

UF headers, and dumps header information to the screen. Only parameters whose value has 

changed from the previous header are dumped, making it easy to trace parameter changes 

through the data set. 

2.2.2 drex.cpp 

drex is the Digital Receiver EXerciser program. It allows an operator to input commands 

and parameters from the console and record data from the ICS-554 Digital Receiver. Data from 

the Digital Receiver may also be dumped to the screen. This program needs to be extended to 

deal with amplitude scaling of the incoming data. 

2.2.3 raw2rawNC.cpp 

raw2rawNC reads raw radar files from RADS and produces a netCDF file with the same 

data content. No changes are made to the data at all, but some of the header information is 

dropped as being not relevant. 

2.2.4 rpInit.cpp 

rpInit is used to maintain Radar Parameter sets. It is typically used before experiments to 

create the parameters that will be used during the experiment. 

2.3 Libraries 

2.3.1 complex.c 

complex is a library program that contains various functions for processing complex 

numbers. 
F-3 

2.3.2 sio_.c 

sio_ is the Sequential I/O package for tape and disk and is a library program that supports 

4-record-4 format. Its purpose is to create an I/O abstraction layer whereby calling routines 

utilize the same functions calls independently of whether the I/O is to tape or to disk. To 

accomplish this, data within disk files is encoded to simulate tape records in the 4-record-4 

format. 

2.4 Classes 

2.4.1 Sockets 

Class Sockets is used to implement Berkeley socket connections between processes on the 

same or different computers using Internet Protocol. A socket connection enables the two 

processes to exchange information. Sockets consists of three classes and an abstract base class, 

Socket, which provides basic services to the other classes. ServerSocket is used on the server 

side to listen for clients trying to connect. When one tries to connect, Connection is used to 

accept the connection. On the client side, ClientSocket is used to connect to a server. 

The classes communicate by sending packets of data. An 8-byte header is added during a 

write, and stripped off on read. The header has two words: the first is a "magic" number flagging 

the beginning of a packet, and the second is the length of the packet, including the header. Thus 

a null packet has a length of eight. 

2.4.2 RadConfig 

RadConfig is used to configure radar programs (or any program) at runtime by extracting 

parameter values from a text file. The text file has entries such as 

NTRG = 256 # parameter NTRG is the number of triggers 

MNEL = 1.5 # parameter MNEL is the minimum elevation angle 

and is created with a text editor. Methods in RadConfig allow you to extract a parameter's value 

by name. The parameter value may be a float, int or char variable. 

2.4.3 PrmtrList 

PrmtrList is useful for maintaining parameter lists that contain different types of parameters. 

Information about the parameter, such as name and type (float, int, etc.), is stored along with the 

actual value of the parameter. This makes it possible to process parameters as a group, with 

methods being provided to list all parameters to the terminal, and update the parameter value 

from the keyboard by entering its name and value. 

2.4.4 Rp2ef 

Rp2ef reads a Radar_Params block, and creates an EF (Extended Format) header from it. 

Only one field is created, the "RD" or "raw data" field. This implementation will put padding 

between the different sub-headers in the UF specification, which could possibly cause a problem 

with some programs that do not (correctly) read the pointers in the sub-headers, and follow them 

to find other sub-headers, or the actual data. 
F-4 

2.4.5 RadParms 

The purpose of RadParms is to provide a means for client programs to access the 

Radar_Params structure without having to know which version of Radar_Params they are 

accessing (by using polymorphism). Currently the Radar_Params exist in versions 0 and 1, and 

so are represented by derived classes RadParms0 and RadParms1 which are not seen by the 

client program. Methods exist for all the common operations one would do on a parameter list, 

such as getting and setting parameters, verifying the validity of a parameter block, checking all 

the parameters, reading a parameter value from the keyboard, printing, etc. Minor revisions 

(which do not change parameter displacements) are handled by incorporating them into the latest 

version. A major revision would necessitate the creation of a new derived class: RadParms2. 

2.4.6 RpBeam 

RpBeam is used to read and manipulate data in RP format, which is the Radar_Params 

structure concatenated with the raw data received from the Digital Receiver. RpBeam uses the 

RadParms class, and so makes available all the parameter manipulation that RadParms provides. 

2.4.7 Thread, Mutex and ConditionVariable 

These three classes are used to make working with pthreads easier and to provide some 

additional error checking. 

3. RC+ AND RIPS SOFTWARE 

3.1 Introduction 

The RC+ code consists of five separate C++ processes; radarReformatter, radarRelay, 

reformatter, writeDisk and xDisplay. DIFS sends the RC+ unit radar data. That data is then 

reformatted, distributed, written to disk, displayed and sent to the RIPS. The RIPS consists of 20 

separate processes that ingest four data streams, calculate the icing product, provide viewers to 

allow display of the products locally and remotely. It also includes a web server that allows 

viewing of data remotely via internet browsers. Throughout both systems, reformatters extract 

or reformat the data and relays act as software fan-out boxes distributing data streams to multiple 

consumers simultaneously. All of the code is C++ or Java. The C++ code runs on either Solaris 

or Linux and the Java code runs under Windows. 

3.2 RC+ 

3.2.1 Programs 

The RC+ subsystem consists of the following processes: radarReformatter, radarRelay, 

reformatter, writeDisk and xDisplay. 

3.2.1.1 radarReformatter 

radarReformatter ingests two radar data streams from the DIFRS; raw data and calculated 

field data for display. This process adds the EF header information and then sends the data to 

radarRelay for distribution. It also includes a control window graphical user interface for 
F-5 

debugging and testing purposes. It interfaces with Motif libraries and allows the user to control 

the data ingest from the four data sources and to set various parameters. 

3.2.1.2 radarRelay 

 radarRelay is analogous to a software fan-out box that allows the data to be sent to multiple 

processes simultaneously in pseudo-real-time using shared memory technology. It ingests the 

display data from the radarReformatter process and sends it to the viewer process, xDisplay and 

the reformatter process. radarRelay also sends the raw data to the writeDisk process. 

3.2.1.3 xDisplay 

The xDisplay process displays the field data in close to real-time as either marching timerange displays, ppi displays or rhi images. It offers the user an interface for changing color 

scales, fields, thresholding and zooming. It uses Xlib and Xview libraries. 

3.2.1.4 Reformatter 

The reformatter program receives display data from the radarRelay process and extracts the 

reflectivity and depolarization data and sends them to the RIPS. These quantities are needed for 

calculating the icing product and for viewing on the RIPS. It also writes the reformatted data to 

disk. 

3.2.1.5 writeDisk 

writeDisk writes raw EF files, field EF files and/or netcdf files to disk. 

3.2.2 Classes 

Although there are numerous classes used in these programs, the following are shared among the 

programs. 

3.2.2.1 Config 

Config calls RadConfig and specifies the variables needed for the particular configuration file 

being read, depending upon the program. 

3.2.2.2 RadConfig 

Same as above. 

3.2.2.3 RadParms 

Same as above 

3.2.3 Libraries 

3.2.3.1 Gdb 

Gdb is a global data base that allows the multiple processes to share information through shared 

memory. 

3.2.3.2 Xpm 

Xpm is a library that stores and retrieves images to and from XPM format and is available on the 

Web. 
F-6 

3.2.3.3 Xlib 

Xlib is the X11R6 library for graphical display calls and is available on the Web. 

3.2.3.4 Xview 

Xview is a wrapper for Xlib calls and is available on the Web. 

3.2.3.5 Gd1_2 

Gd1_2 is a gd library that creates GIF images and is available on the Web. 

3.3 RIPS 

3.3.1 Programs 

RIPS is composed of five relay processes, four RMI servers, an icing algorithm process, two 

viewer processes and four writer processes. Relay processes always relay data from one source 

to one or multiple destinations. RIPS uses three different protocols to relay data: RMI, HTTP 

and TCP/IP socketing. There are two versions of the viewers; one which receives RMI objects 

and one that “gets” HTTP objects. 

3.3.1.1 radarRelay 

Although radarRelay is capable of serving data to multiple clients, it currently only sends radar 

data to the radarRMIServer. 

3.3.1.2 radarRMIServer 

radarRMIServer is an RMI server that serves radar RMI objects to the writeRadar, icingViewer, 

and icingAlg processes. 

3.3.1.3 icingAlg 

icingAlg ingests the data streams from the four instruments and calculates the icing product. The 

icing algorithm is performed on every beam of radar data at every range gate. It uses reflectivity 

and depolarization data from the radar, liquid water from the radiometer and temperature from 

the RUC to calculate an icing product. It sends icing RMI objects to the icingViewer, writeIce 

and to iceRelay. 

3.3.1.4 iceRelay 

iceRelay distributes the icing product from the icingAlg to DataRexx for Web service. 

3.3.1.5 writeRadar 

writeRadar receives RMI radar objects from radarRMIServer and writes raw data files to disk for 

archival. 

3.3.1.6 icingViewer 

There are actually two versions of the icingViewer. One is used on-site and the other off-site. 

The on-site icingViewer process ingests the icing products that are served via RMI by the 

icingAlg. The off-site version of the icingViewer ingests icing products via DataRexx using 

HTTP. Each version consist of three tabbed panes; one for the icing hazard product, one for 

reflectivity and one for depolarization. The process allows the user to select how often the 
F-7 

automatic snapshots should be captured, where they should be written, when to perform a special 

snapshot, which fields to view and what values to use for scaling the fields. 

3.3.1.7 writeIce 

writeIce receives the icing product from the icingAlg process and writes the icing product, 

reflectivity and polarization data to disk for archival 

3.3.1.8 radiomRelay 

radiomRelay is the fan-out box for the radiometer data; it ingests the radiometer data stream and 

serves it to both the radiometerRMIServer and DataRexx. 

3.3.1.9 radiomRMIServer 

radiomRMIServer serves radiometer RMI objects to the writeRadiom, radSurfViewer and 

icingAlg processes. 

3.3.1.10 writeRadiom 

writeRadiom writes the radiometer RMI objects to disk for archival. 

3.3.1.11 radSurfViewer 

There are two versions of the radSurfViewer processe. One is used for on-site and one is used 

for off-site. The on-site version receives and displays the radiometer data and the surface 

meteorological data as RMI objects. The off-site version receives radiometer data and surface 

meteorological data using HTTP from DataRexx. Both versions consist of three separate 

windows. Two display radiometer data as Marching Time Displays, and one displays surface 

meteorological data as a Marching Time Display. The newest data comes in on the left side of 

the screen and the older data moves off to the right. Six radiometer variables are available and 

are plotted against time. These are: liquid water, vapor, temperature, and brightness temperature 

from each of the three channels. The surface meteorological variables available are: humidity, 

pressure, and temperature. The user can select from the available variables and change scale 

values for each variable. GIF or JPEG images can be captured automatically or through 

operator initiation. 

3.3.1.12 RUCRelay 

RUCRelay ingests RUC data and serves it to the RUCRMIServer and DataRexx. This part of 

the system has not been implemented. 

3.3.1.13 RUCRMIServer 

RUCRMIServer receives RUC data from RUCRelay and serves RUC RMI objects to the 

radSurfViewer, writeRUC, and icingAlg processes. This has not been completed. 

3.3.1.14 writeRUC 

writeRUC receives RUC RMI objects from RUCRMIServer and writes RUC data to disk for 

archival. This has not been implemented. 

3.3.1.15 surfaceMetRelay 

surfaceMetRelay sends surface meteorological data to the surfaceMetRMIServer and to 

DataRexx. 
F-8 

3.3.1.16 surfaceMetRMIServer 

surfaceMetRMIServer ingests surface data from surfaceMetRelay and sends surface 

meteorological RMI objects to the writeSurf, icingAlg and radSurfViewer processes. 

3.3.1.17 writeSurf 

writeSurf ingests surface meteorological RMI objects from the surfaceMetRMIServer and writes 

the surface meteorological data to disk for archival. 

3.3.1.18 DataRexx 

DataRexx is a third party product which acts as a real-time data hub. It collects, serves and 

archives data using HTTP. Since DataRexx works across the internet and through firewalls, 

multiple remote clients are able to access the data in near real-time. DataRexx allows multiple 

remote data producer clients to post data to it via HTTP. Currently iceRelay, radiomRelay, and 

surfaceMetRelay post data to DataRexx. RUCRelay will do the same. Data consumer clients 

such as radSurfViewer and icingViewer can then get data from DataRexx via HTTP. 
Appendix G 

G-1

GRADS Software Capabilities

1. INTRODUCTION 

This document outlines and specifies the essential software components required for 

the GRIDS RADS, or GRADS. The GRADS is a multi-level distributed software 

architecture that has been designed for the GRIDS. Its design allows for significant 

changes in hardware and operating systems and its modularity lends itself to functional 

software changes as needed. Several distributed processes comprise the GRADS. They 

not only acquire four separate data streams, but also perform integration, archival and 

display of the data in real-time. The structure easily lends itself to additional data streams 

as required. The system produces data files as well as graphical products in real-time. 

The four data streams in this application are Ka-band radar, radiometer, surface 

meteorological (surface met), and RUC files. 

 Included are descriptions of the four major software subsystems that complete the 

GRADS: the Digital IF Receiver Subsystem (DIFRS), the Radar Control Plus (RC+) 

subsystem, the Remote Integrated Product Server (RIPS) and the Radar Monitor. These 

modules constitute the “on-site” (at-the-radar) system. The DIFRS and Radar Monitor 

software are discussed only as they relate to the RC+ and RIPS software. The DIFRS 

software is discussed in a separate document (Appendix H). The majority of this 

document is devoted to describing the RC+ and the RIPS. The long proven RADS 

(Radar Acquisition and Display System) and POP (Profiler On-line Program) software 

systems were studied, and their desired features have been included in the GRADS 

software design. A description of the “off-site” Web server system and viewers is also 

included. 

2. GRADS 

2.1 Data Flow 

Figure 1 is a graphical overview of the GRADS data flow for the four instruments 

needed to calculate the icing hazard product for the GRIDS and the processes associated 

with each data stream. The radiometer, RUC and surface meteorological data streams 

utilize TCP/IP socket architecture to transmit their data records to the RIPS for Web 

service and viewing. RIPS uses remote method invocation or RMI to share objects 

among its multiple processes. RMI is a means of distributing objects across machines 

and between processes, and works well as long as it does not have to scale firewalls. The 

radar data stream also uses TCP/IP to transmit data to the RC+ processes. The RC+ 

processes employ RMI architecture and shared memory technology to share objects 

between multiple processes and the RIPS. Relay processes, which are found throughout 

the system, are software fan-out boxes and serve the data to one or multiple client 

processes simultaneously. RMI server processes also act as fan-out boxes as they serve 

RMI objects simultaneously to various other RMI processes. 

In order to share objects beyond the firewalls, hypertext transfer protocol or HTTP 

has been shown to work well. In order to accommodate both HTTP and RMI protocols, 
G-2

two versions of the RIPS capitalize on the modular design. Each has a different front-end 

and protocol scheme, yet share multiple classes. The on-site version of the code uses 

RMI communication and the off-site version uses HTTP. (See Section 2.3.) 

Figure 1. Software architecture diagram of GRADS data flow. The architecture 

implements TCP/IP, RMI, and HTTP. 

2.2 On-Site Computers 

The on-site computers, which are physically connected with a Local Area Network 

(LAN), are the DIFRS PC, the Radar Monitor Aux PC, the RC+ PC, the RIPS PC and the 

Firewall PC. Figure 2 shows the data and control flow between these five machines, as 

well as the high level software architecture design. This document will not discuss the 

Firewall PC, as it serves as a gateway to the Internet for the LAN and provides standard 

system security for the LAN. 

HTTP

TCP/IP 

HTTP 

RMI 

GRADS Data Flow Diagram 

TCP/IP

Radar 

Radiometer 

RC+

DIFRS 

Aux PC 

radarReformatter 

radarRelay 

reformatter 

xDisplay 

writeDisk 

RIPS 

(see Figure 3 for details) 

Web 

Server 

RUC + 

Surface Met 

Radiometer 

PC 

TCP/IP 

RMI 

Data 

Viewers 
G-3

Figure 2. System architecture diagram showing control and data flow. Each block 

represents a different computer. 

2.2.1 Data ingest 

In order to calculate the icing hazard product, the four data sources must be ingested. 

The design calls for four separate but simultaneous processes to each ingest one data 

stream and communicate with the icing algorithm. The data are available asynchronously 

and in various formats. They all ultimately flow into the RIPS which calculates the icing 

hazard product and displays the icing product and the raw data products in close to realtime. The four data streams are described in detail below. 

As mentioned earlier, the GRADS ingests these data streams via TCP/IP sockets, 

RMI or HTTP. The communication protocol depends upon the remote sensor’s hardware 

configuration, where it is located, how it communicates and where the RIPS is executing. 

The RIPS allows for the input protocols to be configured and thus can dynamically 

interface with changing input streams. The RIPS executes on-site in the radar, ingesting 

remote sensor data streams via TCP/IP. In the radar, RIPS uses RMI to share objects 

among its internal processes. RC+ uses RMI and shared memory to share its objects 

among its internal processes. 

Since RIPS also runs off-site as a Web application (and thus needs to be able to 

penetrate firewalls), in that configuration it not only ingests remote data via HTTP, but 

the internal processes communicate via HTTP as well. See Section 2.3. 

radar fields radar 

health

radar 

health

met

radiometer 

Web server products 

RUC

raw radar data 

1 Gb/sec link 

IEEE488 radar 

control 

DIFRS 

Digital IF Receiver 

Subsystem 

RC+ 

Radar Control Plus 

RIPS 

Remote Integrated 

Product Server 

radar 

triggers 

XRADS 

GRADS radiometer 

data & 

control 

raw 

RUC 

raw 

met

AUX 

Auxiliary Computer 

Windows 2000 

Firewall 

2 IF channels 

Internet
G-4

Figure 3 details the data flow of the RIPS and the processes which compose the RIPS 

subsystem. It not only illustrates the data flow within RIPS, but the software architecture 

employed as well. The data flow and several of the associated processes are discussed 

below. Further details regarding the RIPS and its processes are found in Section 2.2.3.2. 

Figure 3. RIPS data flow and software architecture. Every arrow going into or out of 

DataRexx is an HTTP connection. All connections out of the RMIServers are RMI and 

all other connections are TCP/IP sockets.

2.2.1.1 Radar data 

The DIFRS sends covariance data to the RC+ subsystem via TCP/IP sockets. 

Covariance data is defined in the GRIDS Covariance Algorithms (Appendix I) document. 

The modular software design easily allows for future additional modes such as spectra. 

radarRMI

Server 

RIPS

radarRelay 

writeRadar icingViewer 

writeRadiom radSurfViewer 

radiomRMI

Server radiomRelay

writeRUC 

surfaceMet

Relay 

RUCRMI

Server 

RUCRelay 

icingAlg iceRelay 

writeIce 

DataRexx 

radSurf icingViewer 

Viewer 

writeSurf 

surfaceMet

RMIServer 
G-5

These modes are selectable by the user. GRADS reads and writes Radar Parameters 

version 1.2, as outlined in Gibson et al (2004). 

Range gate limits are operator enabled. Four to 100 range gates are available in the 

covariance mode. The number of range gates in the usual operating mode is 69. The 

maximum number of triggers is 1,000,000, with a standard operating number of 545,454 

triggers for a 60-second dwell. Typically the data rate will be one beam every 60 to 62.5 

seconds. If time series data is sent from DIFRS for recording and viewing, the data rate is 

approximately 10 Mbytes/sec. 

As in the RADS design, the radarReformatter process (see Figure 1) in RC+ ingests 

two different kinds of data: raw data and display data. Raw data is archived; display data 

is calculated from raw data and is displayed in the radar for the operator. 

radarReformatter sends the display data to the process radarRelay which acts like a 

software fan-out box, which in turn sends the display data simultaneously to both the 

viewer process, called xDisplay, and the reformatter process which extracts and forwards 

reflectivity and depolarization data. At the same time, radarRelay also sends raw data to 

the writeDisk process for archival on disk. The reformatter then sends the reformatted 

data to the RIPS where it is input to the icing algorithm process, icingAlg, and displayed. 

The reformatter also sends the reformatted data to writeDisk for archival, if required. 

File naming conventions include the year, month, day, hour and minute in the file name. 

2.2.1.2 Radiometer data 

Time stamped radiometer data is sent to RIPS from the Radiometer PC every minute 

via a TCP/IP socket. Since the radiometer generates a point every 15-17 seconds, the 

software on the Radiometer PC averages the last four data samples before sending them 

to the RIPS’ process radiometerRelay every minute. The variables sent include 

integrated liquid (which is needed for the icing algorithm), integrated vapor, and 

brightness temperature from each channel. The process writeRadiom in RIPS appends 

radiometer data into hourly files. The size of these files is typically between 

30-50 Kbytes per hour. File naming conventions include the year, month, day, hour and 

minute. The RIPS’ process radiometerRelay also sends the data to the radSurfViewer 

process for display, and the icingAlg process via TCP/IP sockets, RMI and/or HTTP, as 

explained earlier. The radiometer displays are updated as soon as the radSurfViewer 

process receives the data. 

2.2.1.3 RUC data 

RUC model output is served to RIPS from the Radar Monitor process on the AUX 

computer approximately every hour, or as the data becomes available. The timing is 

driven by the update of RUC data on the Web site. Data becomes available on the RIPS 

machine as a file via a TCP/IP socket. Format and filenames of these files must include 

the time and date. A RIPS’ process, RUCRelay, reads RUC data as it becomes available 

and will interpolate the data in space. It then sends the height and temperature data to 

icingAlg via RMI, if on-site, or HTTP if off-site. RUCRelay simultaneously sends the 

RUC data to the radSurfViewer process for viewing and to the writeRUC process which 

archives the data into hourly disk files. 
G-6

2.2.1.4 Surface meteorological data 

Every minute a surface meteorological data record is served to RIPS from the Radar 

Monitor process on the AUX computer via a TCP/IP socket. This data includes 

temperature, humidity and pressure. A process called surfaceMetRelay listens for these 

packets, does some reformatting and sends the packets to multiple processes. 

SurfaceMetRelay can send the minute radiometer data to the icingAlg process via a 

TCP/IP socket or RMI. surfaceMetRelay also sends the surface meteorological data to 

the radSurfViewer for display via RMI, TCP/IP sockets and/or HTTP depending upon the 

implementation writeSurf also receives the data via RMI and archives the data to disk. It 

creates a time stamped file every hour and appends the minute surface meteorological 

data record as it receives it. The format and filenames include the time and date. 

2.2.2 Control Window Graphical user interface 

Although the GRIDS was designed to be run primarily without an operator through 

pre-configured files that are read at startup, the RC+ system process radarReformatter 

includes a graphical user interface (GUI) for debugging and testing purposes. This GUI, 

as shown in Figure 4, is written in C++ with calls to Motif and allows an operator to 

control the data ingest from all four data sources and to set various parameters. Some of 

the functions of the RC+ include turning on and off the various data streams, turning on 

and off the instruments, and positioning the instruments. 

Figure 4 RC+ GUI user interface 

2.2.2.1 Data Ingest control 

As described previously, there are four data streams that need to be ingested by RIPS 

in order to calculate the icing algorithm. The user is able to turn these data streams on 

and off individually by selecting the check boxes on the right hand side of the above 

menu. The icing algorithm is only calculated when all four of the data streams are turned 

on, which is the default. 

2.2.2.2 Data Archival 

A separate archival function has been made available to the user. This switch allows 

the user to turn on and off the archival capabilities. When the archival switch has been 

turned on (which is the default) the four ingested data streams will be archived along with 
G-7

the generated icing, radar and radiometer GIF files and the results of the icing algorithm. 

If the switch is turned off, no files will be written. 

2.2.2.3 Instrument Control 

The user is able to run the radar in continuous scan mode or in a queuing mode. The 

queuing mode allows the user to run various scan control tables consecutively while 

continuous mode runs the same scan control table repeatedly. The user can also pause 

the system. Extensive instrument control, including positioning, and starting and 

stopping the radar and the radiometer, is available. The user can exit RC+ from the “File 

Exit” menu bar option in the RC+GUI. 

2.2.2.4 Radar Parameters 

The user can edit, store, load, list, view and check different individual scan control 

tables, through drop-down menus from the “Edit” and “View” menu bar options. The 

user is able to list the names of the existing scan control tables. These tables allow users 

to indicate which mode they wish to run and to select which parameters to change (e.g., 

housekeeping) and view. The user can select between covariance and spectral modes. 

The user is able to create, edit, store, load, list and run individual queues (lists of scan 

control tables), as well. 

2.2.3 Viewers 

There are three data viewers available in the GRIDS: xDisplay, icingViewer and 

radSurfViewer. They allow users to manipulate and view the data. This section 

describes the numerous options available to the user for creating a custom look at the 

data. The data displays themselves are described in Section 2.2.4. The xDisplay process 

is part of RC+ and allows the user to view radar data in real-time. The icingViewer and 

radSurfViewer are part of the RIPS, and display the icing product, some radar products, 

radiometer products and surface meteorological products in close to real-time. 

2.2.3.1 RC+ Data Viewer 

There is a separate RC+ process called xDisplay that ingests data for graphical 

display and updates in real-time. The xDisplay process is a C++ process which calls XLib 

library functions to display the radar data in real-time on-site. It has its own GUI to allow 

the user to control the viewing of radar data. 

The xDisplay GUI includes options for selecting display type (i.e., Marching TimeRange or A-scope) and field to be viewed. Any field or variable can be displayed on 

either the A-scope or Marching Range-Time display simultaneously. It also allows the 

user to change the minimum and maximum scale for each field, and the maximum range 

and threshold on correlation. It has both an erase, and a GIF or JPEG capture capability. 

There is no exit button in the xDisplay GUI. It communicates with the other processes 

through shared memory and TCP/IP sockets. 

2.2.3.2 RIPS (Remote Integrated Products Server) Data Viewers 

The on-site Remote Integrated Products Server (RIPS) is a Java based system 

consisting of several processes which communicate via remote method invocation (RMI). 
G-8

See Figure 3 for the RIPS data flow diagram. One of its processes, the rmiregistry, is the 

name service for the RMI processes and does not appear in the figure. Some of the relay 

processes, which need to be able to scale firewalls in order to serve data off-site, also 

communicate via HTTP. This is discussed further in Section 2.3. 

RIPS is composed of five relay processes: one for each of the four data streams 

ingested and another one for the derived icing hazard product. It is also composed of 

four RMI servers: one for each of the four data streams ingested. Additionally, it 

includes an icingAlg process which performs the icing algorithm and produces the icing 

product. RIPS includes two viewer processes: radSurfViewer and icingViewer. These 

viewers not only run in the radar van, but can also run through a browser over the 

Internet. They each include a GUI that allows the person viewing the data to change the 

scales, the foreground and background colors, and the variables or fields plotted. They 

allow the user to take snapshots, and to change the periodicity of automatic snapshots. 

2.2.3.2.1 radSurfViewer 

The radSurfViewer ingests radiometer and surface meteorological remote objects 

from the radiometerRelay and the surfaceMetRelay processes. It receives these via RMI 

and TCP/IP sockets if executing on-site, and HTTP if running off-site. It displays them 

in real-time in three panels, updating shortly after the beginning of every minute. The 

first panel includes liquid water, vapor and liquid water temperature. The second panel 

includes brightness temperatures from all available channels, and the third panel displays 

humidity, pressure and temperature from the surface met device. 

All of the variables may be selected and scaled. Figure 5 shows the user interface for 

selecting and scaling variables. The user selects which variables are to be viewed and 

then specifies the minimum and maximum values for the y-axis. The user can also select 

the number of minutes between automatic JPEG or GIF snapshots (or turn the feature off) 

and can select special snapshots. Background colors and line colors may be selected by 

the user. This process reads a previously configured file upon startup. The configuration 

file defines default scales, how many channels, minutes between automatic snapshots, 

fields to be displayed, and the rest of the features offered in the user interface. 
G-9

Figure 5. The GUI panel in RIPS’ radSurfViewer allows the user to select fields and 

scale them for display. 

2.2.3.2.2 icingViewer 

The icingViewer process ingests the derived icing products that are served by the 

icingRelay process. It consists of three tabbed panes: one for the icing hazard product, 

one for reflectivity (ZShD), and one for depolarization (ZdrD). The viewer allows the 

user to select how often the automatic snapshots should be captured, when to perform a 

special snapshot, which fields to view and what values to use for scaling the fields. At 

start-up it reads the same configuration file as the radSurfViewer. 

2.2.4 Data Displays 

As discussed above, the viewers allow the user to manipulate the data as shown on 

the screen. The different types of displays available are described in this section. When 

discussing displays, it is necessary to differentiate between variables and fields. A 

variable is a function only of time (e.g., integrated liquid water). Variables like this are 

plotted as the y-coordinate versus time (x-coordinate). A field is a function of range and 

time, such as radar reflectivity. Here the magnitude of the field is indicated by a color on 

a plot of range (y-coordinate) vs. time (x-coordinate). 

There are four types of display available for selection: Marching Time-Range 

(formerly called BSCAN), Time-Range or A-scope (including RUC model output), 

Marching Time-Icing Displays and Marching Time-Radiometer Displays. 

2.2.4.1 RC+ 

2.2.4.1.1 Radar Data Displays 

For radar data, the nineteen possible fields are available one at a time for the 

Marching Range-Time and A-scope displays in the RC+ process xDisplay. The displays 

are updated in real-time as the radar data is ingested. The user can change fields and the 

scales corresponding to each field. The user can also adjust the height or range of the 

image. Updates to the displays are made in real-time. 
G-10

 Marching Range-Time displays present a maximum of 600 beams or ten hours of 

radar data, with the oldest data scrolling off the left-hand side of the display. A-scope 

displays are not normalized as seen in current RADS displays. The user can change the 

scale of x for spectra and A-scopes, and max range (or y) in Marching Time-Range 

displays and A-scope displays. 

Figure 6 is an example of data from the AIRSII project. This Marching Range-Time 

display is as seen on the RC+ machine in the radar van. The field displayed is reflectivity 

or ZShD. The x-axis is time and the y-axis is height in kilometers. The color identifies 

the reflectivity in dBZ. 

Figure 6. Example of the RC+ radar data Marching Time-Range Display of reflectivity, 

ZShD. Time is on the x axis (hh:mm:ss), height or range in kilometers is on the y axis. 

Figure 7 is an example of an AScope display as it would be seen in the radar van. The xaxis is range in kilometers and the y-axis is uncorrected correlation. The transmitted 

polarization of the radar was slant and the correlation was derived from data received in 

the horizontal channel 
G-11

Figure 7. Example of an AScope display showing range in km on the x-axis vs 

uncorrected correlation from transmitting slant polarization and received on the h 

channel. 

2.2.4.2 RIPS 

There are two processes in the RIPS which allow the user to view and manipulate 

three types of displays: radSurfViewer and icingViewer. The display types are RangeTime Displays of RUC model output, Marching Icing Displays, and Marching 

Radiometer Displays. These processes can ingest data using RMI from the on-site RIPS 

system or HTTP data as served by the DataRexx web server off-site. DataRexx is 

discussed in more detail in Section 2.3. 

The display types can be viewed simultaneously, but there can only be one field of 

each type viewed at any one time. Although the Icing Display has the same appearance 

as the Marching Range-Time display, it is designed to be displayed on a dedicated 

console to avoid congestion on the screen. The GRADS is designed to show another 

Marching Range-Time display of a user selected variable on another monitor 

simultaneously. 

2.2.4.2.1 Radar Data Displays and Icing Hazard Displays 

In the on-site RIPS, the icingViewer process displays radar and icing hazard data. 

Three fields are available to the Marching Icing Displays: reflectivity, icing hazard and 

depolarization. These are updated every minute or whenever data is received. JPEG or 

GIF snapshots can be captured automatically or through user initiation. 
G-12

The icing hazard data is viewed as a Marching Range-Time display that is updated 

every minute. It displays up to ten hours of data, with the oldest data scrolling off the 

left-hand side of the panel. One local copy is served on the console and one to another 

dedicated monitor. Changing the scale of the icing field will only affect the local copy of 

the image. A GIF or JPEG file of the current icing display on the dedicated monitor is 

sent to the home office or operations center, in our case Boulder, every minute to be 

served over the Web no longer than three minutes behind real-time. Since a copy of the 

data is also sent over the internet to the PSD Web server, similar displays which are 

updated in close to real-time, are available at the PSD GRIDS website as well. See 

Section 2.3 for further details on the Web server used for GRIDS. The icing product 

currently has three values (and thus three colors) available to it. No hazard is shown in 

green, possible hazard is shown as yellow and hazard is red. 

Each of the two radar fields and the icing hazard field may be selected and the scale 

may be changed for each. Figure 8, below, is an example of the reflectivity field, ZShD, 

from the AIRSII experiment (Schneider et al 2004) as displayed by the icingViewer. 

The x-axis is time, the y-axis is height in kilometers, and the colors indicate reflectivity in 

dBZ. The blank vertical bars occurred when the prototype radar was running a nonvertical mode for testing. 
G-13

Figure 8. Example of the RIPS Radar Data Marching Range-Time Displays. 

Reflectivity, ZShD, in dBZ is shown. X is time, with about eight hours shown, and Y is 

height or range in kilometers. 

2.2.4.2.2 Radiometer Data Displays 

Radiometer data is viewed as Marching Time-Radiometer Displays through the RIPS 

process radSurfViewer. Six variables are available to the Marching Time-Radiometer 

Displays. These are liquid water, water vapor, liquid water temperature, and the 

brightness temperature from each of the three channels. The user is able to select from 

the available variables and change scale values for each variable. 

Plots are x-y displays of integrated liquid water, integrated water vapor and liquid 

water temperature overlaid on one panel and the three brightness temperatures overlaid 

on another panel. These variables are plotted against eight hours of time (on the x-axis). 

The six variables are plotted in different colors, configurable by the user before start-up 

or during data collection. This display type operates like the other display types in that 

the user is allowed to select the six radiometer variables from the select field menu bar 

option. Corresponding scales and ranges for those variables can be selected as well. The 

displays are updated every minute or as new radiometer data is ingested. GIF or JPEG 

images can be captured automatically or through operator initiation. 
G-14

Figure 9 is an example of the radiometer display types and the surface 

meteorological data display types generated by the radSurfViewer in RIPS. The three 

separate panels are displayed together in one window. This example is for illustration 

purposes only and contains simulated data. 

Figure 9. Example of the RIPS Radiometer and Surface Meteorological Marching Time 

Data Displays 

2.2.4.2.3 Surface Meteorological Data Display 

The radSurfViewer process also displays the surface meteorological data. Three 

variables are available to the Marching Time-Surface Met Displays: humidity, pressure 
G-15

and temperature. The surface meteorological panel is updated every minute with new 

data and contains eight hours of data. The time between two vertical bars is one hour. 

The oldest data scrolls off the left-hand side of the display. The user is again able to 

select from the three available variables and can change the scale for each. GIF or JPEG 

images can be captured automatically or through operator initiation. In Figure 9 the 

surface meteorology data display is the bottom panel and illustrates the concept with 

simulated data. 

2.2.4.2.4 RUC Model Display 

The RUC model displays are profiles of temperature vs. height. They are shown in 

an x-y plot. The user is able to change the scale for this variable. The displays are 

updated every hour or whenever new RUC data is ingested by the AUX Radar Monitor 

subsystem. 

2.2.4.3 Perform the icing algorithm every minute and update the icing displays 

A separate process in the RIPS called icingAlg, performs the icing algorithm every 

minute using the most recent data from the RUC mode, the radar, the radiometer and 

surface meteorological input. The icing displays are updated every minute with the latest 

icing product as described in Section 2.2.4.2.1 above. The icing algorithm is performed 

on every beam of radar data, at every range gate. It uses reflectivity and depolarization 

data from the radar, liquid water from the radiometer and temperature from the RUC. 

The surface meteorological data are used as a diagnostic tool to aid the operator, but are 

not used in the icing product calculation.

2.2.4.4 Automatically capture the icing displays 

Every minute the most recent icing display is automatically captured as a GIF or 

JPEG image and sent to a Web server in Boulder that makes them available on the Web. 

These are updated every minute and are less than three minutes behind real-time. Also, 

the icing data is sent to the PSD Web server to be displayed in close to real-time through 

a browser for remote access. 

2.2.4.5 Write data to disk 

RC+ stores raw radar data in EF (Extended UF; Gibson et al 2004) disk files. The 

SWTM parameter (sweep time) determines the size and length of time of the radar file. 

Covariance radar files require 56 bytes per range gate. With 69 gates and 60-second 

beams, the files are 291,840 bytes/hour (with a 1000-byte header on each beam). All file 

names conform to a standard that indicates type, location (radar parameter RDID) and 

time. It also archives GIF and JPEG snapshot files of the radar fields automatically. This 

feature, including which fields and how often to automatically capture the image, is 

configured by the user before run time. 

RIPS stores radiometer data, icing model output, snapshot files of radar, icing 

hazard, radiometer, surface met and RUC marching displays, RUC input files, and 

surface meteorological data as separate files. File names include time and date. A 

problem with all of these files arriving asynchronously is determining which files were 
G-16

used in a particular icing model calculation. Therefore the icing model filename must 

include the creation time of the input files as well. 

The RIPS process writeRadiom saves radiometer data to a separate file. It appends a 

radiometer data record onto this file every minute, when data is available. At the end of 

the hour the file will be closed and sent to the archival system. The data rate generation 

for these files has been shown to be 30-50 Kbytes per hour. 

Surface meteorological data is handled in the same way as radiometer data, with a 

RIPS process called writeSurf. Data can be ingested via either RMI or a TCP/IP socket 

every minute and appended to an hourly file. 

Typically a new GIF or JPEG file of the icing display is generated every minute and 

is sent to the Web server at that time. Once an hour, the half hour GIF or JPEG files for 

the previous hour are served to the archival system. These files are about 110 Kbytes 

each. 

RUC files are archived as they come into the RIPS system. Real-time automatic 

capture of RUC images is currently not available. 

RIPS stores all the data for up to 48 hours, in case the link to the archival system is 

unavailable. 

2.2.4.6 Archive the data 

There is a script that uses Secure Copy (SCP) to copy the icing, radar, radiometer, 

RUC, GIF images and surface meteorological data to the archival system once an hour, 

unless the Internet link is down. Once the files have been archived, they are moved to 

another part of the disk for playback purposes. 

Although a GIF or JPEG file of the icing display can be sent to the server every 

minute, only two of the half hour GIF or JPEG files are archived each hour. 

2.2.4.7 Monitor disk usage and take appropriate action 

Since a month’s worth of data takes less than a gigabyte of storage space, the disk 

scrubbing process only deletes files after they are at least four weeks old. 

2.3 Off-Site Computers 

2.3.1 Web Service of icing displays 

 Each of the relay processes in the RIPS can ship data through the firewall and to the 

DataRexx server via HTTP. DataRexx is a third party product written and distributed by 

Bear Peak Software, Inc. DataRexx acts as a real-time data hub that collects, serves and 

archives data using HTTP. Since DataRexx works across the Internet and through 

firewalls, multiple remote clients are able to access the GRIDS data in near real-time. 

DataRexx allows multiple remote data producer clients to “post” data to it via HTTP. 

Data consumer clients can then “get” data from DataRexx via HTTP. 

Figure 10 is the system overview of the DataRexx system. The data formatter takes 

raw data and packages it into archive format. Data relays “post” data through HTTP to 
G-17

the server. Clients are Java applications for viewing and archival. The RIPS system 

supplies all of these modules. Versions of radSurfViewer and icingViewer running on 

end-user desktop machines ingest data via HTTP from the DataRexx server. They 

display radiometer, surface met, radar, RUC and icing hazard data, making this data 

remotely accessible in near real-time. Once the viewer clients are started, the marching 

time displays are updated every minute or whenever data is available. 

Figure 10. System Overview of the DataRexx server. 

2.3.2 Web Archival of data 

DataRexx archives all of the data streams that it receives. Another script archives 

the GIF or JPEG files as they are produced or sent from the on-site computers once an 

hour, unless the network link is down. Once the files have been archived, they will be 

moved to another part of the disk for playback purposes. 

Although a GIF or JPEG file of the icing display will be sent to the server every 

minute, only two of the files will be archived each hour; one at the hour and one at the 

half-hour. 
G-18

3. ADDITIONAL FEATURES 

3.1 Playback capabilities 

The system is able to run in playback mode as well as in real-time mode. Playback 

is able to read from local data files that were previously archived. It displays the data, 

much as it does in real-time. Since it does not need to be a VME system, or to have any 

specialized hardware, it can run on the AUX system, or another Linux system at the same 

time as the GRADS, or on a stand-alone Linux system. The icing images, radiometer 

displays and variables, RUC displays, surface data and displays, plus the nineteen radar 

display fields and images need to be made available in playback mode, thus allowing the 

archived RUC data, radar data, radiometer data, surface meteorological data and icing 

model data to be ingested by the playback machine. 

3.2 On-line help 

On-line help in the form of an on-line manual is available. 

3.3 Reacting to system health messages 

Reacting to system health messages includes power failure recovery and the ability 

to resume scheduled activities without operator intervention. Other items to be 

considered include how often health messages should be sent and in what format. 

Trouble messages come over to RC+ from the Radar Monitor with an error code. A 

tabular structure might be used to categorize responses for each trouble message. For 

every kind of message there might be several possible actions that can be defined (note 

that several actions could be selected for each problem although some are mutually 

exclusive) including ignore, print message on console, e-mail message, stop and restart, 

permanently stop, execute a special routine and continue, execute a special routine and 

restart, etc. GRADS runs autonomously and remote monitoring is available via a Web 

interface. 

3.4 Spectral processing and displays 

This mode has not been defined yet and will have its own associated parameters, 

including windowing, number of spectral points, spectral averaging, and DC filtering. 

For display purposes, it will be its own display type, and will include a cascade or 

waterfall window as well as displays for user selected ranges. The user will need to be 

able to change the color scale, but may not change ranges. 

3.5 Radar/radiometer control 

This includes pause/start, position and shutdown. The Radar Monitor process on the 

AUX system does the actual shutdown of the radar, which can be initiated by either UPS 

software or by RC+. Computer shutdown is independent of shutdown of the instruments. 
Appendix H 

H-1

Implementation of the Digital Receiver System

1. INTRODUCTION 

The digital receiver system takes two channels of IF (co-polarized and cross-polarized 

channels), digitizes them, digitally mixes them to baseband, runs the signals through several 

stages of digital filtering, and then transfers them to the host computer. The data transferred is 

the standard I (in-phase) and Q (quadrature-phase) signals that would be output by an analog 

receiver. All of this is performed on one PCI card in the host computer. 

There are several advantages to the digital approach, including elimination of much of the 

hardware in the receiver, flexibility in bandwidth, elimination of several receiver calibration 

steps, and improved performance. The chief performance enhancements are elimination of the 

spectral image and an increased dynamic range. 

2. OVERVIEW 

The digital receiver system will take a 60 MHz IF and digitize it at an 80 MHz rate 

(sampling at less than the Nyquist rate is called harmonic sampling). These frequencies allow 

the use of a common IF frequency and minimize the effects of aliasing. (See equation 7.51 in 

Frerking 1994, where N = 1.) The digitizing process translates the IF frequency to 20 MHz in 

the digital domain. The digital mixer in the digital receiver chip is then used to mix the signal 

down to baseband. 

3. DIGITAL RECEIVER CARD 

The digital receiver is a PCI card contained in the DIFRS computer chassis. This card is the 

model ICS-554B-2-MN from Interactive Circuits and Systems and contains two A/D channels 

and four digital receiver integrated circuits. The digital receiver ICs are the model GC4016 from 

Graychip and each of them contains four receiver channels, which yields a total of 16 receiver 

channels on the ICS-554B. The reader should refer to both manufacturers’ literature for 

complete detail, but to facilitate the following discussion Figure 1 reproduces a diagram of a 

single receiver channel in the GC4016. 
H-2

Figure 1. A single receiver channel in the Graychip 4016. 

Note that a single channel is capable of taking an IF signal and complex demodulating it to 

baseband using a Numerically Controlled Oscillator. The complex demodulation process 

generates two channels (In-phase and Quadrature) that feed the digital filters, which are capable 

of processing I and Q in parallel. Looking at the minimum decimation values of the digital 

filters, we see that the minimum total decimation is 32. With an 80 MHz sampling rate, this 

means that the maximum data rate at the output is 2.5 MHz, which may not be fast enough for 

some radar work. However channels on the chip can be combined to increase the data rate. 

Split-I/Q mode accomplishes this by using one channel to process the real half of the 

complex data, and a second channel to process the imaginary half. Since only half as much data 

is going through a single channel, the filters can run twice as fast and so the minimum value of 

the CIC filter decimation becomes four, rather than eight. (Although the diagram suggests that 

there are two parallel channels in each filter, there is actually one channel that is timemultiplexed to appear as two.) This gives an output data rate of 5 MHz. 

Still more throughput can be achieved by sharing the data flow with the two unused 

channels on the chip. (This is called the wideband downconvert mode.) These extra two 

channels operate in Split-I/Q mode also, but now one pair of channels processes the evennumbered samples and the other pair processes the odd-numbered samples. These samples are 

combined in the output circuit so it is as if the PFIR decimate-by-two doesn’t exist. Now the 

minimum decimation of the data becomes eight and so, with an 80 MHz sampling rate, the 

maximum output data rate is 10 MHz, which means an IF bandwidth of 5 MHz is possible. This 

is the mode that is implemented in the software. 

4. GRIDS DIGITAL RECEIVER SYSTEM 

4.1 Digital Filtering Strategy 

Because of the wide bandwidth typically required in pulsed radars as compared to usual 

communications systems, care must be taken to provide adequate bandwidth in the digital 

receiver system which was primarily designed for narrowband use. This means keeping the 

decimation values as small as possible using the means discussed in the previous section. Once 
H-3

this has been achieved, it is a simple matter to narrow the bandwidth when required for long 

pulses. 

The other radar requirement imposed on the receiver system is the matched filter, which 

means that the shape of the filter response should approximate a Gaussian curve (Doviak and 

Zrnic 1993) whose width is a function of the transmitted pulse width. This Gaussian response is 

performed in the CFIR where the signal has been decimated as little as possible. 

At this point, all the necessary filtering has been done and so the PFIR filter is essentially 

bypassed by programming the appropriate coefficients to just pass the data through. The 

Resampler is now used only to decimate the data to get to the desired range gate spacing. 

4.2 Radar Timing Generator 

The digital receiver card will operate in conjunction with a Radar Timing Generator (RTG) 

card and a frequency synthesizer card which will be housed in the same chassis. Figure 2 shows 

a diagram of the digital receiver system and the IF Modulator/Receiver. The frequency 

synthesizer card (Echotek ECSG-1R3ADC-PMC) is used to synthesize an 80 MHz sampling 

signal needed by the digital receiver card from a 20 MHz signal generated in the IF 

Modulator/Receiver. This same 20 MHz signal drives the RTG and is used to synthesize other 

required timing signals in the system. 

Figure 2. Digital receiver system. 

Table 1 shows all the timing signals generated by the RTG. The names of the signals are 

similar to the ones used in the MMCR. In fact, truncating the names to the first two letters yields 

the MMCR nomenclature. Some of the signals are not needed in the current configuration, such 

as RPOL, which was to switch receive channels in a single receiver configuration. 

The RTG was implemented on a VME board in an earlier configuration of GRIDS using 

three Altera EPLDs. The new RTG will use a single Altera part on a PMC card (Acromag 

IP1K110-2412). 

cross IF

cross IF

20 MHz

 TXPL, BLNK, SYNC, SMGT 

1 Gbit 

Ethernet

20 MHz

main IF

IF XMIT IF Modulator/Receiver 

DIFRS – Digital IF Rcvr 

Subsystem 

Linux PC

Freq Synth 

Card

DR Card RTG Card 

80 MHz DTRG main IF

IF main, IF 

cross, log main, 

SYNC, SMGT 

O’scope 

TREN, TXPL, BLNK 
H-4

Table 1. External signal list for Radar Timing Generator 

# Short 

Signal Name 

Full Signal Name Function Used By Controlling 

Parameters (50 ns resolution unless indicated) 

Comment 

1 SYNC Sync Oscilloscope trigger Display only PRPR (1 us), 

NSYC 

MMCR used absence ochannel receive. Now dmay occur every TXPLevery 4th TXPL to maincompatibility. 

2 TREN Transmit-Receive 

Envelope 

Brackets transmit pulse Pulse controller TRDL, TRWD 

3 BLNK Blank Receiver blanking IF Mod/Rcvr, Pulse 

Controller 

BLDL , BLWD 

4 TMEN Transmit Envelope Brackets transmit pulse Pulse Controller TMDL, TMWD Not used in MMCR 5 TXPL Transmit Pulse Transmit pulse IF Mod/Rcvr, Pulse 

Controller 

TXDL, TXWD 

6 SMGT Sample Sampling gates Display only DLAY (1 us), 

SPAC, NGAT 

GRIDS digitizer only u

7 RPOL Received 

Polarization 

Indicates whether to receive on co- or cross-channel 

Pulse controller RPDL, 

RPOL[0:7] 

Eight-bit shift register c

8 PHAS Phase Pulse-coding phase 

modulation within radar trigger 

IF Mod/Rcvr PHWD, NCBT Possible future 

9 SCYC Start Cycle O’scope trigger. Resets SYNC 

and RPOL. 

Display only NTRG Useful for debugging 

10 SPHS System Phase Switches LO phase in a 

pseudo-random sequence to suppress multi-trip echoes. 

not used Possible future 

11 DTRG Digitizer Trigger Precedes first sampling gate Digital Receiver DLAY (same as 

in SMGT) 

12 DCLK Digitizer Clock not used not used none 20 MHz clock 
H-5

5. SOFTWARE 

A C++ class called DigRcvr was designed to handle setup and operation of the Digital 

Receiver card. The objective was to give the user of the class an interface that was radar 

oriented and that hid the complexity of the Digital Receiver. A radar-oriented interface means 

that radar parameters such as range gate spacing, number of range gates and radar trigger length 

are used rather than parameters such as bandwidth and decimation. Because not all 

combinations of parameters are possible, a method called tryParams( ) was written that takes 

input parameters and adjusts them to something that the hardware can actually do. Once the 

digital receiver is started, it runs continuously. A method called readData( ) is used to read the I 

and Q data, which is double-buffered within the class. Data that is not read in time is discarded 

so as not to disrupt the data flow within the class. 

5.1 Description of class DigRcvr 

Class DigRcvr runs the ICS-554B digital receiver card in a way suitable for radar. The card 

is operated in the wideband downconvert mode and produces 24-bit I and Q data, each left 

justified in a 32-bit word. It is assumed that two IF channels (main and cross) will be used. 

Gaussian bandpass filters are implemented using the digital filtering capability on the Graychips. 

Data is digitized by the A/D's at the sampleRate (typ. 80 MHz for a 60 MHz IF). The data is 

mixed against ncoFreq (Numerically Controlled Oscillator) in the Graychip to bring it down to 

baseband. Decimation in the first filter (CIC) is kept to the minimum value of 4 so as to allow as 

many taps as possible in subsequent filters. The next filter (CFIR) implements the Gaussianshaped bandpass response. The filtering capability of the PFIR is not used and so the data merely 

passes through it. The Resampler is used to perform decimations, but its digital filtering 

capability is also not used. 

Because of limitations in the memory (FIFO) of the card, beams may be needed to be 

divided into sub-beams and collected together to reach the length required so that this class can 

return a complete beam of data. For a 50% FIFO utilization, which seems to perform well, the 

maximum product of gates*triggers must be <= 32768 for one acquisition cycle (sub-beam). 

When this product is exceeded, sub-beams must be used. 

tryParams( ) calculates the size of the sub-beams and so the calling program may not get the 

exact number of gates and triggers that it requests, but the actual values used are returned by 

tryParams. Then setParams( ) is used to set these parameters into the digital receiver. Once 

startData( ) is called, data collection begins. Incoming data is double-buffered but readData( ) 

must be called at a sufficient rate or else data will be lost. It can be determined if data has been 

lost by looking at beamCntr in the packet header. 

5.1.1 Data Types 

Units used for numeric values are always in base units unless otherwise indicated. For 

instance, range gate spacing (spac) is in seconds so 1*10-6 is a typical value. 

/* PCKT_HDR is at the beginning of each beam. DigRcvr fills in beamCntr, 

 * pcktCntr (= beamCntr), secs and nsec. Other information is filled in by 

 * other programs. pcktCntr becomes useful downstream if beam averaging is 
H-6

 * performed. 

 */ 

struct PCKT_HDR // PCKT_HDR appears at the beginning of data 

{ 

 uint pcktCntr; // DR's packet counter 

 uint beamCntr; // DR's beam counter 

 uint secs; // Unix time in seconds 

 uint nsec; // ..and nanoseconds 

 float azim; // azimuth of beam in degrees 

 float elev; // elevation of beam in degrees 

}; 

/* DigRcvrInit contains parameters to initialize the Dig Rcvr card. These are 

 * parameters that are usually fixed for a given hardware configuration. The 

 * default set may be accessed by declaring 'extern DigRcvrInit initStruct' 

 * in the user program. 

 */ 

struct DigRcvrInit 

{ 

 float ncoFreq; // frequency of local oscillator in tuner in Hz 

 float sampleRate; // sampling rate of A/D's in MHz 

 float fifoUtil; // desired FIFO utilization in percent 

}; 

/* TmParams is used by tryParams() and setParams(). tryParams() takes the 

 * first seven parameters and calculates the last two. Some of the first seven 

 * may be altered to achievable values. setParams() uses all parameters as input 

 * only and returns an error code if the parameters are unachievable. 

 */ 

struct TmParams 

{ 

 float rtln; // radar trigger length; used to set bandwidth of rcvr 

 float prpl; // pair repetition period (used for checks only) 

 float prps; // inter-pair period (used for checks only) 

 float dlay; // delay from radar trigger to first range gate (checks only) 

 float spac; // range gate spacing 

 uint ngat; // number of range gates (must be multiple of 4) 

 uint ntrg; // number of triggers in beam 

 uint nsbeams; // number of sub-beams 

 uint ntrgsb; // number of triggers in sub-beam 

}; 

// This struct contains derived parameters and is returned from setParams(). 

struct DRparams 

{ 

 int error; // error code. 0 if no error. 

 uint buffSize; // size of ICS554 beam buffer in bytes 

 // size = (8*ngat*nsbeams*ntrgsb) + sizeof(PCKT_HDR) 

 float fifoUtil; // actual FIFO utilization in percent 

 uint dec; // total decimation of Graychip. Multiple of 8 

}; 

// Format of a single complex sample returned from DigRcvr 

// Order of real & imag picked to match order from ICS-554 

struct DRsample 

{ 

 int imag; // imaginary part 

 int real; // real part. 24-bit left justified in 32-bit word 

}; 

5.1.2 Public Methods 

// DigRcvr class 

// User must call setParams() before calling startData(). It is recommended that 
H-7

// tryParams() be called before setParams() because it will adjust the 

// timing parameters to achievable values. 

class DigRcvr 

{ 

public: 

 DigRcvr(DigRcvrInit init); // basic DR init for fixed parameters 

 TmParams tryParams(const TmParams* pIndy); // adjusts parameters 

 DRparams setParams(TmParams); // initializes the DR 

 int startData( ); // starts DSP and data collection 

 int readData(void* pBuff); // get data from DSP 

 bool isDataRunning( ); // determine if dataThread is active 

 void stopData( ); // stop DR and cancel thread 

 void prntDDCconfig( ); // prints the GC configuration (debug) 

 ~DigRcvr( ); // close & unmap 

}; 

Methods are listed in the order that they are normally called. 

DigRcvr(DigRcvrInit init); 

Constructor. Opens the drive and gets a file descriptor for it. Sets defaults for basic digital receiver 

parameters when called in this form. Other parameters that depend on TmParams are set later by 

setParams( ). 

TmParams tryParams(const TmParams* pIndy); 

Takes the first seven TmParams parameters and adjusts some of them to achiveable values. The last 

two parameters (the sub-beam parameters) are calculated by tryParams. This method should be called 

before setParams( ) so that the parameters are correct. 

DRparams setParams(TmParams); 

Completes the initialization of the digital receiver with the timing parameters. At this point, the 

receiver is primed to acquire data. 

int startData( ); 

Starts the digital receiver. startData( ) returns quickly but spins off a thread that continues to run and 

acquire data until stopData( ) is called. This thread uses a double buffer to continuously acquire data 

that may be accessed by calling readData( ). If readData( ) is not called soon enough, entire beams 

will be lost. Data is acquired continuously with no gaps until stopData( ) is called. 

bool isDataRunning( ); 

Checks whether the digital receiver is running. 

int readData(DRsample* pBuff); 

Data is taken from the last buffer acquired and placed in the buffer pointed to by pBuff. A 

PCKT_HDR packet will be at the beginning of the data. The data is in time order and consists of a 

series of type DRsample. The number of triggers and range gates must be used to demultiplex the 

data. This call will normally block while waiting for data. If readData( ) is called often enough, no 

data will be lost. If it is not called in a timely manner, entire beams will be missed. This can be 

detected by looking at the beam counter in the packet header. 

void stopData( ); 

Stops the digital receiver. 
H-8

~DigRcvr( ); 

Destructor. Stops the digital receiver if necessary and deallocates memory. 

5.1.3 Decimation Algorithm 

This algorithm calculates the decimation to use to achieve, as nearly as possible, a desired gate 

spacing. A derivation is given for the algorithm followed by the algorithm itself. 

Independent variables: 

spac -- desired gate spacing 

rate -- A/D sampling rate (typ. 80 MHz) 

Dependent variables: 

dec -- total decimation in Graychip 

spac2 -- achievable gate spacing 

Decimation in Graychip 

CIC ----> CFIR ----> PFIR ----> ReSamp ----> out

4 2 1 n 

dec = 8n decimations in chip are fixed, except for resampler decimation which is n 

1 <= n <= 31 hardware limitation in Graychip 

rate/dec = 1/spac this expresses the desired result 

dec = rate*spac 

8n = rate*spac 

n = rate*spac/8 

1 <= rate*spac/8 <= 31 apply the hardware limitation 

Algorithm: 

1) n = floor(rate*spac/8 + 0.5) floor function truncates fractional part of a number 

2) Check that 1 <= n <= 31; if not pick 1 or 31 for n, whichever is appropriate 

3) dec = 8n 

4) spac2 = dec/rate 

Example: 

If rate = 80 MHz and spac = 1.55 μs, then n = 16, dec = 128 and spac2 = 1.6 μs. 

6. STATUS 

At the termination of the project, the DigRcvr class was working although not extensively 

tested. Also the issue of the gain through the Graychip had not been addressed. Gain can be set 

through the Coarse Gain block and with a fine gain setting in the PFIR. 

DigRcvr was tested using a program called drex for Digital Receiver EXerciser. The drex 

program exercises DigRcvr by allowing an operator to input various parameters and observe the 

results as a listing of the output from the digital receiver. 
H-9

7. FUTURE PLANS 

Several tasks remain to finish the digital receiver system. On the hardware side, the Radar 

Timing Generator design needs to be ported to the PCI-based card. This is not expected to be a 

long task since the design has been implemented in three chips on a VME card and will be ported 

to a single chip on a PCI card with much more capability than the three chips put together. The 

chips are all from the same manufacturer. A diagnostic program was also written under Solaris 

that should port easily to Linux. 

On the software side, gain in the DigRcvr class needs to be addressed. Then a class to 

control the RTG needs to be written. There is code for a VME RTG class (RtgCntrl) that was 

written that can be leveraged to help create this. Also a class that drives the digital synthesizer 

board needs to be written. 

 Then a class needs to be written that ties together the digital receiver, the RTG and the 

frequency synthesizer so that there is one interface to all the digital receiver hardware. This 

would then be used in drex to test the complete digital receiver system. 

Finally, the main DIFRS program needs to be written. This program will take instructions 

and parameters over a socket from the RC+ computer, calculate covariances from the digital 

receiver data, and transfer the results to the RC+ computer. This program will not have any 

console control since X-windows operation increases interrupt latency significantly and 

interferes with the proper operation of the digital receiver. 

Appendix I 

I-1

GRIDS Covariance Algorithms

1. INTRODUCTION 

This document specifies the covariance-based algorithm that will be performed in real-time 

in GRIDS. This algorithm is commonly called the pulse-pair algorithm because it measures the 

phase difference between the received signal from a pair of radar pulses. With a very high 

signal-to-noise ratio, this phase difference and the time between the pulses is all that is required 

to calculate the Doppler shift and consequently the radial velocity of the target. In practice, the 

signals from many pairs of pulses are summed together to reduce the uncertainty of the estimate 

to an acceptable level. 

Included is a brief introduction to covariances in the following section to give some context 

to the algorithm definitions. 

2. COMPLEX COVARIANCES AND SINE WAVES 

Some of the signal processing used in GRADS is based on “pulse-pair” or covariance 

algorithms. The radar transmits pairs of pulses in the manner shown in Figure 1.

Figure 1. Transmitter pulses and a single range gate are shown for a double-pulse modulation 

scheme. 

A single range gate is illustrated at a delay tr from the transmitted pulses. Samples at the 

short pulse spacing, TS, are used to give an estimate of the Doppler velocity. Results at the long 

pulse spacing, TL, are summed together and used to reduce the uncertainty of the estimate. The 

advantage of this type of “double-pulse” modulation over equally spaced pulses is that by 

reducing TS, the unambiguous velocity limit of the radar can be increased without increasing the 

transmitted power, which is limited by the characteristics of the transmitter tube. This is 

particularly useful for short-wavelength radars. The disadvantage is that the unambiguous range 

is limited by how close the pulses in the pair are. However, TS can always be changed to half of 

TL, if desired, to give equally spaced pulses. 

Radar 

pulse 

Range 

gate 

TS TS 

TL 

tr tr tr tr 
I-2

In the following discussion, whenever a covariance product is calculated, it is done from 

pulses in the pair, while the sums of these products are performed over the pairs. The time TL is 

not significant in the calculations, except indirectly in that increasing it reduces the number of 

samples taken in a given time and reduces the beneficial effects of averaging. 

The complex covariance function is used for many purposes in statistics, but here it is used 

only to calculate the phase change between two points in complex sine waves, and the power 

present in a complex sine wave. Complex quantities are in bold in the following equations and 

in Table 3. 

Consider two complex sequences, Xi and Yi. Then the general definition of complex 

covariance is given by 

( ) ( ) ( ) i n i n

*

i

*

XY n i + − + = − ⋅ X X Y Y R , (Equation E-1) 

where Xi and Yi represent complex sequences and n represents the number of lags to use in 

calculating the covariance. The “< >” notation represents the average and “*” denotes the 

complex conjugate. 

This equation may be rewritten to give 

( ) i n

*

i n i

*

XY n = i + − +

X Y X Y R . (Equation E-2)

In GRIDS, Xi and Yi are usually the same channel, which is the digitized representation of a 

voltage. This signal will be denoted Ei. Since we are now using an auto-covariance, we can 

simplify our nomenclature to 

( ) i n

*

i n i

* n = i + − +

E E E E R . (Equation E-3)

For the zero-lag covariance, where i ranges over every pulse 

( ) i

*

i

2

i i

*

i i

*

i 0 E E E E E E E R = − = − , 

or 

( ) 2

i

2

i R 0 E E = − . (Equation E-4) 

Note that R(0) is real-valued. This represents the average of the “power” (voltage squared) 

minus the average DC value squared. In other words, R(0) is the DC-corrected “power.” 

R(1) may be written as follows, where i increments by two (for pairs): 

( ) i 1

*

i 1 i

*

i 1 = + − +

E E E E R , (Equation E-5) 

where Ei+1 is the signal from the second pulse in the pair. 

Now calculate R(1) for a complex sine wave as defined by S j iT

i Ae ω = E , 

so 

TS j (i 1)

i 1 Ae ω +

+ = E . 

Then 

( ) S S S S S S S TS j iT j (i 1)T j iT j (i 1)T 2 j T j iT j iT j 1 Ae Ae Ae Ae A e Ae Ae e − ω ω + − ω ω + ω − ω ω ω R = − = −

( ) ( ) S S S S S S S 2 j T 2 j T j iT j iT 2 j T j iT j iT 1 A e A e e e A e 1 e e ω ω − ω ω ω − ω ω R = − = − (Equation E-6) 
I-3

( ) 1 A e ( ) 1 iTS j iTS iTS j iT = 2 j TS − ω − ω ω + ω ω R cos sin cos sin 

( ) ( ) 2

S

2

S

2 j T 1 A e 1 iT iT = S − ω − ω ω R cos sin . 

Therefore, 

( ) ( ) arg R 1 = ωTS . (Equation E-7)

Since the angular frequency, ω, is related to the Doppler shift, this demonstrates how the 

Doppler velocity may be obtained, at least for the case of a sine wave. More elaborate 

derivations for signals plus noise may be found in the literature, but this illustrates the basic idea. 

In GRIDS, the covariance sums are calculated on the Radar Data Acquisition Computer. 

We take the equation 

i n

*

i n i

*

i (n) R = E E + − E E +

and partition it as follows. Let 

( ) i n

* n = i +

B E E and 

( ) i n n = +

A E . 

Then 

() () () n (n) 0 n * B A A R = − ⋅ , 

so 

() () () () () ( ) * 2

R 0 B 0 0 0 B 0 0 A A A = − ⋅ = − and 

() ( ) () 1 (1) 0 1 * B A A R . = − ⋅

Currently, n can be either 0 or 1, so we calculate the quantities A0, A1, B0 and B1, where 

A0 is the complex sum from the first pulse in the pair, and A1 is the complex sum from the 

second pulse in the pair. B0 is defined to be the sum over both the first and second pulse in the 

pair, and B1 is just B(1), which leads to the following equations: 

( ) 2 2

2

1 R(0) B0 A 0 A 1 = − + (Equation E-8) 

( ) B 1 A 0 * A 1 R 1 = − ⋅ . (Equation E-9) 

Note that A0 and A1 are both zero if there is no DC component in the time series. A DC 

component is caused by ground clutter in the echo and inclusion of the terms in the above 

equations removes the effects of ground clutter. Calculating the A’s and B’s separately and 

passing them on to the next step allows calculation of both DC-corrected and non-DC-corrected 

fields. Non-DC-corrected fields are useful for producing ground clutter maps, since normally 

ground targets would be suppressed by the DC correction. 

3. APPLICATION OF COVARIANCE FUNCTIONS TO THE GRIDS RADAR 

The GRIDS radar will transmit a single circular polarization and receive simultaneously a 

co-polarized signal and a cross-polarized signal. We will adopt the convention of using a “c” to 

represent the signal received on the co-polarized channel, and an “x” to represent the signal 

received on the cross-polarized channel. 
I-4

Covariances of the form R(0), signifying zero lags, are formed from summing over every 

co- or cross-polarized sample in the beam, that is, both the first and second pulses of the pair. 

Covariances of the form R(1), signifying one lag, are formed from summing over every co- or 

cross-polarized pair in the beam, where the two factors in the covariance are taken from the first 

and second pulse of the pair. 

The covariances we will use are shown in the table below. Complex quantities are in bold. 

Table 1. Covariance products calculated 

Symbol Description Used to calculate: 

Rcc(0) co-polarized “power” co-polarized power 

Rxx(0) cross-polarized “power” cross-polarized power 

Rcc(1) co-polarized covariance, one lag pulse-pair velocity from 

the co-polarized channel 

Rxx(1) cross-polarized covariance, one lag pulse-pair velocity from 

the cross-polarized 

channel 

By calculating B’s and A’s separately, we make it possible to produce “non-DC-corrected” 

values of various quantities, such as received power, which may be useful for calibration. The 

table below shows “data products”, which are quantities that are recorded. Total memory 

required is 56 bytes for each range gate. 

Table 2. Recorded data products 

Calculated 

variable 

name 

Mathematical 

symbol 

Description Required for 

Ac0 Ac(0) average of co-polarized samples from 

first pulse of pair 

DC correction 

Ax0 Ax(0) average of cross-polarized samples from 

first pulse of pair 

DC correction 

Ac1 Ac(1) average of co-polarized samples from 

second pulse of pair 

DC correction 

Ax1 Ax(1) average of cross-polarized samples from 

second pulse of pair 

DC correction 

Bcc0 Bcc(0) uncorrected co-polarized covariance, 

zero lag 

power & pulse-pair width 

Bxx0 Bxx(0) uncorrected cross-polarized covariance, 

zero lag 

power & pulse-pair width 

Bcc1 Bcc(1) uncorrected co-polarized covariance, 

one lag 

pulse-pair velocity & 

width 

Bxx1 Bxx(1) uncorrected cross-polarized covariance, 

one lag 

pulse-pair velocity & 

width 
I-5

The following table shows the display (discarded) products. A “U” at the end of a name means 

that the value has been calculated from non-DC-corrected covariances. All of these quantities 

are calculated for each range gate and are available for display. 

Table 3. Display products (not recorded) 

Description Derived from 

co-polarized 

channel 

Derived from 

crosspolarized 

channel 

Derived 

from 

both 

channels 

Units 

velocity Vc Vx m/s 

velocity spread Wc Wx m2/s2

correlation Cc Cx none 

intensity (power at receiver output) Ic, IcU Ix, IxU dBm 

power (power at antenna terminals) Pc, PcU Px, PxU dBm 

Reflectivity factor Zc, ZcU Zx, ZxU dBZ 

circular depolarization ratio CDR dB 

The following equations will be used to calculate DC-corrected covariances from the 

uncorrected covariances: 

[ ] ( ) ( )

[ ] ( ) ( )

(1) (1) (0) (1)

(1) (1) (0) (1)

0 1

2

1 (0) (0)

0 1

2

1 (0) (0)

*

*

2 2

2 2

xx xx x x

cc cc c c

xx xx x x

cc cc c c

R B

R B

R B A A

R B A A

A A

A A

= − •

= − •

= − +

= − +

The following equations are shown with R’s for the covariances, but will also be used with 

B’s to calculate uncorrected quantities where required. In the following equations z0 is the 

receiver impedance (50 ohms), rnh and rnv are the co-polarized and cross-polarized receiver 

gains, c0 is the speed of light, khRC and kvRC are the co- and cross-polarized radar constants, fT is 

the transmit frequency, TS is the time between the first and second pulses in the pair, R’(0) is 

covariance with receiver noise, and r is the range in meters. 
I-6

arg( ) (1) 4

0

cc

T TS f

c Vc R

π

− = arg( ) (1) 4

0

xx

T TS f

c Vx R

π

− = m/s 

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− ′ = − (0) (0)

(1) 1

8 2 2 2

2

0

cc cc

cc

fT TS R R

c Wc

R

π ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− ′ = − (0) (0)

(1) 1

8 2 2 2

2

0

xx xx

xx

fT TS R R

c Wx

R

π

m2

/s2

(0)

(1)

cc

cc

R

Cc

R = (0)

(1)

xx

xx

R

Cx

R = none 

30 (0) 10 log

0

+⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = • z

R Ic cc 30 (0) 10 log

0

+⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = • z

R Ix xx dBm at 

rcvr 

output 

Pc = Ic − rnh Px = Ix − rnv dBm at 

antenna 

Zc = Pc + khRC + 20 • log(r) − 60 60 Zx = Px + kvRC + 20 • log(r) − dBZ 

CDR = Pc − Px dB