In-situ instrumentation
Weaknesses
1. Accuracy and dynamic range of in-situ instru-
1. Characterization of the 3-D scale of cloud physi-
ments need improvement, especially for SLD.
cal properties at the submesoscale, continental,
2. Small, accurate, and inexpensive in-situ instru-
and global scales, including temperature, liquid-
ments are unavailable.
water content, and drop sizes.
3. 3-D remote-sensing resolution, range, and angu-
2. Characterization of SLD.
lar scanning area need specification.
3. Accurate, reliable, inexpensive in-situ instrumen-
4. Characteristics of clouds critical to flight in icing
tation.
on many airframes in many conditions are poorly
4. There is no objective, weather-based icing index.
understood.
5. Further testing of airfoils needed under a variety
of weather and operating conditions.
General
6. Specifications must be developed for remote-
1. We can currently only speculate about the ideal
sensing systems.
remote sensor scanning range needed--distance
7. Characterization of test beds needed.
and angular, accuracy, and 3-D sensing resolu-
8. FAR 25, Appendix C, must be extended.
tion needed.
2. There is no index of icing conditions by intensity
General goals
as a function of weather condition alone, inde-
1. Define conditions within which clouds are mixed
pendent of aircraft type.
phase, that is, have ice crystals, because mixed-
3. Characteristics of clouds critical to flight from
phase situations indicate that supercooled liquid
flight tests, tunnel tests, and numerical models,
water exists.
in a spectrum of meteorological conditions from
2. Characterize 3-D spatial scales of icing by icing
wide variety of aircraft, are not available.
potential and microphysical properties.
4. There is no objective system for reporting icing
3. Assess continental-scale and global-scale icing
potential independent of aircraft type.
patterns, frequencies, and intensities to determine
5. There is currently no objective system of report-
needs for commercial aviation and military avia-
ing areas of icing or no icing that is accurate in
tion in potential operational theaters.
intensity, position, and time.
4. Characterize SLD conditions with regard to fre-
6. Weather forecasters and numerical models cur-
quency, range of conditions, synoptic situations,
rently do not have available objective, timely tem-
and continental and global patterns.
perature, liquid-water content, and drop-size
5. Reanalyze old flight data with improved instru-
information that is accurate in position.
ment-correction algorithms.
7. A meteorology-based icing intensity standard is
6. Fly new research flights to develop specifications
needed.
for remote-sensing systems.
8. Need to expand FAR 25, Appendix C to include
7. Develop small, turnkey, inexpensive in-situ
SLD.
instruments for aircraft.
8. Investigate feasibility of dynamically calibrating
SENSING REQUIREMENTS KNOWLEDGE
remote-sensing systems from in-situ sensors.
Strengths
9. Develop sensing specifications for remote-sensing
1. General climatologies of icing are available.
systems.
2. Information desired to be downlinked to forecast-
10. Develop meteorologically indexed icing intensi-
ers is known.
ty scale.
3. There is considerable data and confidence in FAR
11. Assess synoptic meteorological conditions within
25, Appendix C, icing-condition characterization.
which remote sensing would be most practical.
4. General research instrumentation is generally ade-
12. Identify appropriate test beds.
quate.
5. Appropriate test platforms are available.
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