Christopher J. Johnston, David J. Serke, and Daniel R. Adriaansen

National Center for Atmospheric Research, Boulder, Colorado

Andrew L. Reehorst

Glenn Research Center, Cleveland, Ohio

Marcia K. Politovich, Cory A. Wolff, and Frank McDonough

National Center for Atmospheric Research, Boulder, Colorado

Comparison of In-Situ, Model and 

Ground Based In-Flight Icing Severity 

NASA/TM—2011-217141

December 2011

Paper number 10.2
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Christopher J. Johnston, David J. Serke, and Daniel R. Adriaansen

National Center for Atmospheric Research, Boulder, Colorado

Andrew L. Reehorst

Glenn Research Center, Cleveland, Ohio

Marcia K. Politovich, Cory A. Wolff, and Frank McDonough

National Center for Atmospheric Research, Boulder, Colorado

Comparison of In-Situ, Model and 

Ground Based In-Flight Icing Severity 

NASA/TM—2011-217141

December 2011

Paper number 10.2

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135

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NASA/TM—2011-217141 1

Comparison of In-Situ, Model and Ground Based In-Flight Icing Severity 

Christopher J. Johnston, David J. Serke, and Daniel R. Adriaansen

National Center for Atmospheric Research

Boulder, Colorado 80307

Andrew L. Reehorst

National Aeronautics and Space Administration

Glenn Research Center

Cleveland, Ohio 44135

Marcia K. Politovich, Cory A. Wolff, and Frank McDonough

National Center for Atmospheric Research

Boulder, Colorado 80307

1.0 Introduction

In-flight icing is a significant hazard for the aviation industry. It occurs when supercooled liquid water (SLW) comes in 

contact with, and freezes to, the leading surfaces of an aircraft. 

This can significantly alter the aircraft’s aerodynamic 

properties by increasing the amount of drag on the aircraft,

and reducing the lift. Since practical airborne remote detection 

hardware has not yet been developed, a ground-based 

detection system that can provide information to all aircraft 

entering and departing a terminal area (Fig. 1) is a key element 

in facilitating icing avoidance (Serke et al., 2010). 

Currently there are two systems that are being developed for 

the detection of in-flight icing. The first detection system is the 

NASA Icing Remote Sensing System (NIRSS) a testbed that 

integrates three vertically pointing sensors; a Vaisala Laser 

Ceilometer, a Metek Ka-band radar, and a Radiometrics Corporation 23-channel radiometer (Fig. 2), (Reehorst et al., 2006). 

The multichannel microwave radiometer has the ability to 

derive integrated liquid water (ILW), atmospheric water vapor 

and temperature profiles (Solheim et al., 1998). A Vaisala laser 

ceilometer is used to define cloud base heights, and a Metek Kaband radar is used to delineate cloud top and base heights. 

NIRSS combines the ILW, radar reflectivity, temperature 

profile, and cloud top and base heights to determine the 

presence of in-flight icing conditions in the atmosphere.

The second in-flight icing detection system is the Current 

Icing Product (CIP) which was developed at the National 

Center for Atmospheric Research. This system combines 

visible and infrared satellite imagery, radar reflectivity, 

lightning observations, Pilot Reports (PIREPs) and standard 

ground-based weather observations with numerical model 

output to produce a gridded, hourly, three dimensional 

representation of icing probability and severity (Bernstein et 

al., 2005). Each horizontal grid point of CIP is based on a 

20 km by 20 km Rapid Update Cycle (RUC) model grid point. 

First developed during the winter of 1997/98, CIP became an 

operational National Weather Service product in 2002. 

Icing-related PIREPs are voluntary reports made by pilots to 

report on the presence or absence of in-flight icing conditions 

and other weather-related conditions. Both a subjective icing 

severity and icing type (rime, clear or mixed) are included. 

PIREP reports of no icing are useful as well, since the absence 

of icing is important information. The shortcomings of PIREPs 

are well documented and include non-uniformity in time or 

space and contamination by errors in location, altitude and time 

(Brown et al., 1997; Kelsch and Wharton, 1996). PIREPs can 

sometimes be inaccurate due to time lags before the pilot reports 

the observed icing condition, and whether he or she reports the 

correct altitude and location. The reported severity is also 

somewhat subjective as it can vary based on aircraft type, phase 

of flight, and pilot experience. Nevertheless, PIREPs are our 

only means of in-situ diagnoses of actual atmospheric conditions encountered by pilots and their aircraft in the absence of 

expensive icing research flights or specially instrumented fleet 

aircraft. The objective of this study is to examine how the 

testbed NIRSS icing severity product and the operational CIP 

severity product compare to PIREPs of icing severity, and how 

the NIRSS and CIP compare to each other.

2.0 Methodology

A 3-year database of CIP, NIRSS and PIREP data was 

compiled focusing on winter periods from early November 

2008 to late March 2010. During these three winter seasons, 

917 icing PIREPS were collected within 40 km of the NIRSS 

system located in Cleveland, Ohio. CIP icing severity output 

from the nearest RUC gridpoint to the NIRSS location was 

archived and icing severity values were extracted at the time 

and height of each icing PIREP. A similar process was 

conducted for NIRSS severity output. If there was no icing 

data at the exact PIREP altitude, a vertical search was 

performed for the nearest altitude with icing severity data. 

Once this temporal and spatial matching was completed for all 

PIREPs, a statistical comparison was begun. Analysis 

occurred from the ground level to ~ 30,000 ft (or 9,144 m). 

For this study, a PIREP reported over a range of heights is 

treated as multiple PIREPs spread over 1000 ft increments 

(Wolff et al., 2010).
NASA/TM—2011-217141 2

Figure 1.–NIRSS in-flight icing detection concept.

Figure 2.—Image of the NIRSS hardware located at the NASA 

Glenn Research Center in Cleveland, Ohio.

Figure 3.—0000 UTC surface pressure (yellow lines, [mb]), 

cloud top temperature (color bar, [°C]) and frontal analysis for December 15, 2009.

3.0 Analysis and Discussion

3.1 Case Study Comparison—December 15, 

2009

An example icing case study is presented here to illustrate 

how the comparison of NIRSS, CIP and PIREP severity 

looked for a single event. In the next section, statistics for 

3 years of such cases are discussed.

On December 15, 2009, at 0000 UTC, a surface low was 

dominant over the central Great Lakes region (Fig. 3). The 

warm front extended from the southeast portion of Lake 

Huron eastward into southeastern New York. The cold front 

was oriented from north central Ohio through southwest Ohio. 

The cloud top temperatures over the Cleveland area were 

between –5 and –15 °C (color scale). This temperature range 

has been shown in previous research to be conducive to 

supercooled liquid water (Rogers and Floyd, 1989).

At approximately 0300 UTC, the cold front passed through 

Cleveland, Ohio. In the hours following the cold frontal 

passage, drizzle and rain fell over the metropolitan area, which 

changed to snow by 1500 UTC.

Figure 4 shows time versus height of icing severity from 

NIRSS (top), PIREPs (top numerals) and CIP (bottom) for 

December 15, 2009. A zero to eight scale for icing severity 

was used for these plots, and the comparisons throughout the 

rest of this study where zero is no icing, one is trace amount of 

icing, two to three is light, four to five is moderate and six to 

eight is heavy. 

Twenty-eight positive icing PIREPs were recorded between 

1100 and 2200 UTC around Cleveland. In addition, three 

negative PIREPS were recorded during this time period. 

NIRSS diagnosed significant icing between 0900 and 

2400 UTC from 1 to 6 kft AGL. CIP diagnosed icing from 

0900 to 2400 UTC as well, from roughly 1 to 9 kft AGL. For 

this case, the temporal variability and the magnitude of the 

severities generally match between the two products despite 

the fact that CIP has a 1-hr time resolution and NIRSS has 

one-minute resolution. CIP has a conservative cloud top and 

base estimate scheme (done purposely to insure thorough 

warnings).

3.2 3-Year Archive Comparison

In the previous section we explored the comparison of icing 

products for a day-long icing case. This section will be a 

statistical intercomparison of all three icing detection methods 

for the full 3-year study period. Similar to the case study 

presented above, the closest NIRSS and CIP icing severity 

measurements were found to each of the 917 PIREPs within 

40 km of the NIRSS location. The matched severity categories 

were plotted in Figures 5 to 7. 

No icing
NASA/TM—2011-217141 3

Figure 4.—Time [hh] versus height [kft] plots of NIRSS (top, color scale), PIREP (top, red numerals) and CIP (bottom, 

color scale) icing severity from December 15, 2009 from 0000 UTC to 2400 UTC.

NIRSS versus PIREP severity is shown in Figure 5, with 

one-to-one severity correlation bins highlighted in orange. The 

numbers in each bin represent the total number of icing 

severity matchups recorded for the 3-year period. A linear 

best-fit line is overlain in blue. Taking the square root of the 

resulting R-squared value gives NIRSS a severity category 

correlation coefficient of 0.35 to the PIREP severity category. 

NIRSS appears to do well locating negative PIREPs, as well 

as finding moderate icing PIREPs. Very few severe or heavy 

PIREPs were reported during this time period. There are a 

significant number of positive PIREPs that NIRSS identifies 

as negative severity, possibly due to the high time resolution 

of NIRSS’s ILW algorithm when viewing localized SLW 

cases.

CIP versus PIREP severity is shown in Figure 6. A linear 

best-fit line is again overlain in blue, with CIP having a 

severity category correlation of 0.21 to the PIREP severity 

category. CIP and NIRSS both do well at finding PIREPs from 

light to moderate values. CIP seems to correctly identify a 

much smaller fraction of negative PIREPs than NIRSS. This 

could be a result of the conservative cloud base and top 

diagnosis in CIP.

NIRSS versus CIP severity is shown in Figure 7. The operational CIP product is treated as ‘truth’ in this comparison, as 

NIRSS is still a testbed. A linear best-fit line is again shown in 

blue, with NIRSS having a severity category correlation of 

0.18 to the CIP severity category. There seems to be a 

significant spread in the collocated severity values between 

the two products during the 3-year study period. This spread is 

likely due to the difference in temporal resolution of the 

products, and the fact that the two products arrive at hazard 

estimates based on different input datasets.

8 0 0 0 0 0 0 0 0 0

N 7 0 0 0 13 0 0 0 0 0

I 6 1 1 0 18 4 29 0 0 0

R 5 1 7 0 28 8 23 0 0 0

S 4 1 5 0 46 7 15 0 0 0

S 3 6 18 0 95 18 22 0 0 0

2 9 20 0 71 3 28 0 0 1

1 28 10 1 71 12 14 0 0 2

0 115 19 0 93 16 34 4 0 0

0 1 2 3 4 5 6 7 8

PIREP

Figure 5.—Overall PIREP severity versus NIRSS severity.

NIRSS

CIP

[kft]

[kft]

[hh]

[hh]
NASA/TM—2011-217141 4

8 0 0 0 0 0 0 0 0 0

7 1 1 0 5 0 4 0 0 0

6 4 0 0 1 1 1 0 0 0

C 5 5 10 0 21 7 13 0 0 3

I 4 12 13 0 78 20 43 0 0 0

P 3 22 14 0 94 20 35 0 0 0

2 32 12 1 97 13 40 0 0 0

1 38 23 0 92 6 7 0 0 0

0 47 4 0 47 0 22 4 0 0

0 1 2 3 4 5 6 7 8

PIREP

Figure 6.—Overall PIREP severity versus CIP severity.

8 0 0 0 0 0 0 0 0 0

N 7 2 6 5 0 0 0 0 0 0

I 6 5 4 8 11 15 7 1 2 0

R 5 12 7 7 15 14 8 1 3 0

S 4 7 9 13 17 21 6 1 0 0

S 3 9 32 36 37 35 9 0 1 0

2 14 18 41 30 23 6 0 0 0

1 3 29 42 27 20 10 3 4 0

0 76 61 43 0 38 13 1 1 0

0 1 2 3 4 5 6 7 8

CIP

Figure 7.—Overall CIP versus NIRSS severity.

Another useful statistic is the probability that each product 

will detect negative and positive icing PIREPs, or the 

Probability of Detection (POD). These statistics are termed 

PODn and PODy, respectively. To get PODn, the fraction of 

negative icing PIREPs that the respective product identifies as 

negative icing is determined and then divided by the total 

cases where PIREP severity is equal to zero (Eq. (1)). 

PODn = (Total Cases product icing severity = 0 when PIREP severity = 0) (1)

(Total cases PIREP icing severity = 0)

Similarly, PODy is the fraction of positive icing PIREPs of any 

category (one through eight) that the respective product 

identifies as positive icing divided by the total cases where 

PIREP severity is greater than zero (Eq. (2)). 

PODy = (Total cases product icing severity > 0 when PIREP severity >0) (2)

(Total cases PIREP icing severity >0)

For the 3-year study period, PODy and PODn were calculated for the product comparisons shown in Figures 5 to 7. The 

results are shown in Table 1.

TABLE 1.—OVERALL PODn AND PODy STATISTICS

[N versus P (NIRSS versus PIREPS), C versus P 

(CIP versus PIREPS), N versus C (NIRSS versus CIP)]

N versus P C versus P N versus C

PODy 0.78 0.90 0.79

PODn 0.71 0.29 0.59

NIRSS detected greater than 70 percent of both positive and 

negative PIREPs. CIP detected 90 percent of positive PIREPs 

but only 29 percent negative PIREPs. The percentage of the 

time averaged vertical profile that a product has identified a 

positive severity value is termed the warning volume. The 

warning volume for this study is calculated from the surface to 

the average height of the tropopause. A successful product 

must find an optimal balance between POD yes and no and 

warning volume because it would not be very useful for a 

product to have a PODy of 1.0 (perfect icing detection) if the 

entire column is warned on at all times. Ideally, a product 

would have a maximized PODy and PODn with a minimized 

warning volume. For the 3-year study period, NIRSS had a 

mean warning volume of 13 percent, and CIP had a mean 

warning volume of 34 percent. CIP detected 10 percent more 

positive PIREPs in over twice the warning volume. Furthermore, CIP’s high warning volume causes it to classify regions 

as positive icing where they should be devoid of icing, based 

on negative PIREPs.

Summary

In-flight icing detection is crucial to achieving a high safety 

standard for the national fleet of commercial and general 

aviation aircraft. In this study a comparison was done showing 

the quantitative severity categories of negative and positive 

icing PIREPs to the quantitative icing severity derived from 

the prototype NIRSS icing detection algorithm and operational 

CIP icing algorithm. An icing case study from December 15, 

2009, over the NIRSS location in Cleveland, Ohio, was 

discussed to illustrate how the PIREP and icing product 

severities were compared. A statistical analysis over the full 

3-year study period found that NIRSS detected in-flight icing 

and negative icing at least as good as CIP when compared to 

all PIREPs within a 40 km radius. NIRSS detected almost 

80 percent of positive PIREPs and over 70 percent of negative 

PIREPs in a relatively smaller warning volume. CIP detected 

slightly more positive PIREPs than NIRSS but did fairly poor 

in detecting negative PIREPs. This occurred in a warning 

volume over twice the percent of NIRSS’s warning volume. 

CIP did very well at detecting positive PIREPs. NIRSS 

displayed respectable probabilities of icing detection with 

lower warning volumes than CIP. This is due to NIRSS having 

a higher time resolution and utilizing physically based vertical 

profiles of ILW, temperature and radar reflectivity. Therefore, 
NASA/TM—2011-217141 5

the NIRSS testbed in-flight icing severity product seems to be 

at least as good as CIP. A shortfall of NIRSS is that it 

currently lacks volumetric scanning capability. This is being 

addressed by the addition of a 1° beamwidth multichannel 

scanning radiometer (Serke et al., 2010). Future work with 

NIRSS will include exploring Doppler fall velocities to detect 

possible freezing drizzle and freezing rain, and comparing 

NIRSS hazard detection to the polarimetric data from future 

upgraded NEXRAD.

References

Bernstein, B., McDonough, F., Politovich, M., Brown, B., 

Ratvasky, T., Miller, D., Wolff, C., and Cunning, G., “Current Icing Potential: Algorithm description and comparison 

to aircraft observations,” Journal of Applied Meteorology, 

44, pp. 969-986, 2005.

Brown, B.G., G. Thompson, R.T. Bruintjes, R. Bullock, and T. 

Kane, 1997: Intercomparison of in-flight icing algorithms. 

Part II: Statistical verification results. Wea. Forecasting, 12,

890-914.

Kelsch, M., and L. Wharton, 1996: Comparing PIREPs with 

NAWAU turbulence and icing forecasts: Issues and results. 

Wea. Forecasting, 11, 385-390.

Reehorst, A., Politovich, M.K., Zednik, S., Isaac, G.A., and

Cober, S., 2006: Progress in the Development of Practical 

Remote Detection of Icing Conditions (NASA/TM—2006-

214242). 

Rogers, R.R., M.K. Floyd: A short course in cloud physics. 

Pergamon Press, Oxford, England.

Serke, D.J., Beaty, P., Reehorst, A.L., Kennedy, P., Solheim, 

F., Ware, R., Politovich, M.K., Brunkow, D. and Bowie, R. 

“A New Narrowbeam Multi-Frequency Scanning Radiometer and its Application to In-Flight Icing Detection,” AMS 

14th Conference on Aviation, Range, and Aerospace Meteorology (ARAM), 2010.

Solheim, F., Godwin, J., Westwater, E., Han, Y., Keihm, S., 

Marsh, K., and Ware, R., “Radiometric profiling of temperature, water vapor and cloud liquid water using various 

inversion methods,” Radio Sci., 33, pp. 393-404, 1998.

Wolff, C. A., McDonough, F. “A Comparison of WRF-RR 

and RUC Forecasts of Aircraft Icing Conditions,” AMS 14th

Conference on Aviation, Range, and Aerospace Meteorology (ARAM), 2010.
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14. ABSTRACT

As an aircraft flies through supercooled liquid water, the liquid freezes instantaneously to the airframe thus altering its lift, drag, and weight 

characteristics. In-flight icing is a contributing factor to many aviation accidents, and the reliable detection of this hazard is a fundamental 

concern to aviation safety. The scientific community has recently developed products to provide in-flight icing warnings. NASA's Icing 

Remote Sensing System (NIRSS) deploys a vertically--pointing Ka--band radar, a laser ceilometer, and a profiling multi-channel microwave 

radiometer for the diagnosis of terminal area in-flight icing hazards with high spatial and temporal resolution. NCAR’s Current Icing 

Product (CIP) combines several meteorological inputs to produce a gridded, three-dimensional depiction of icing severity on an hourly 

basis. Pilot reports are the best and only source of information on in-situ icing conditions encountered by an aircraft. The goal of this 

analysis was to ascertain how the testbed NIRSS icing severity product and the operational CIP severity product compare to pilot reports of 

icing severity, and how NIRSS and CIP compare to each other. This study revealed that the icing severity product from the ground-based 

NASA testbed system compared very favorably with the operational model-based product and pilot reported in-situ icing. 

15. SUBJECT TERMS

Flight safety; Aircraft icing; Remote sensing; Numerical weather forecasting 

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