Gerald B. Fitzgerald

gbf@mitre.org

29APR2002

Organization: G036Project: 0702V250 1J

Fade Mitigation on Broadcast Satellite Links Using Climatological and Meteorological Forecast

Data

04/19/23 2

Overview

• Problem definition

• Compute Radio Frequency (RF) attenuation for weather-sensitive Satellite Communications (SATCOM) links, on a global basis, with emphasis on ocean areas

• Past approach

• Design the SATCOM system to handle expected worst-case weather

• Use climatology to estimate weather parameters of concern

• Use empirical models, based on climatological data and long-baseline observations of signal strength, to model RF attenuation

• Current research

• Operate the SATCOM system based on expected daily weather (mitigate rain “fades”)

• Use Numerical Weather Prediction (NWP) and satellite-derived observational data to estimate weather parameters

• Adjust attenuation models by assimilating recorded signal strength observations

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Current Approach: The System Design Phase

• Some radio frequencies, particularly Extremely High Frequency (EHF), are very subject to attenuation by atmospheric gases, clouds, and rain

• Attenuation may be approximated by a piecewise-linear weighted sum of functions of (in decreasing order of contribution):

• Rainfall rate• Surface water vapor density• Vertically integrated cloud water content

• SATCOM systems (transmitter powers, antenna diameters, receiver sensitivities) must be sized to accommodate a reasonable guess as to the worst weather through which they are expected to operate…

• … So we employ climatological data to estimate the worst-case attenuation. (Surface air temperature, surface pressure and freezing height are also required by attenuation models.)

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Current GDM* Model Hierarchy

Link Budget

Linear loss sum Weighted loss sum

Rain loss

(DAH***)

Cloud loss

(ITU 840**)

Gas loss(ITU 676)

G/T

Rainfall rate

(ITU 837)

Cloud H20 content

(ITU 840)

Air temp

(COADS)

H20 vapor density

(ITU 836)

Worst-month

loss adjustment

(ITU 841)

O2 Loss H20 vapor loss

676 H2O loss L.K. H2O loss

* Global Broadcast Service (GBS) Data Mapper

**ITU: International Telecommunications Union ( ITU 840 == “ITU-R P.840” recommendation*** Dissanayoke, Alnutt & Haidara, ” IEEE Trans. On Antennas and Propagation, Vol. 45. No. 10, October 1997

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Data Sets: ESA Rainrate Grid

• From European Center for Medium-Range Weather Forecasting 15-year reanalysis effort

• A data model, as is its predecessor, ITU 837 and the Rainzone model

• Annual

• 1 degree cell size

• Computed (variable) exceedances

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Data Sets: Surface Water Vapor Density

• Units: gm/m^3

• From ITU836

• For ITU 676, gas loss model

• Annual and seasonal

• 1 degree cell size

• Mean supplied

• Variance rule supplied

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Data Sets: Total Cloud Water Content

• Units: kg/m^2

• From ITU 840-2

• For cloud loss model (also ITU 840-2)

• Annual only (assumed uniform)

• 1.5 degree cell size

• 12 exceedances supplied

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Data Sets: Ocean Surface Temperature

• From NOAA Comprehensive Ocean-Air Data Set (COADS)

• For ITU-676 gas loss model

• 5 degree cell size

• From 1992 monthly data

• Seasonal, annual averages

• Post-processed

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Link Budget Outputs: NASA ACTS* 99% Exceedence, Annual

*Advanced Communications Technology Satellite, a Ka-band (EHF) SATCOM relay

The ACTS Advanced Mobile Terminal (AMT) is usable where the color is green (good) through yellow (just barely).

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Link Budget Outputs: NASA ACTS 99% Exceedence, Worst-Month

AMT would have trouble in worst-month conditions. This composite colors each grid cell by its own worst month, based on humidity - so this is not a map of any one month’s performance

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Current Research: The Operations Phase

• A working SATCOM system delivers a specific, usable Bit Error Rate (BER - 10-5 or better), at a specific data rate. Lower data rates require less power.

• Because SATCOM systems are sized to deliver a usable BER in near worst-case weather, they have more than enough power to “burn through” more typical weather.

• This extra power, on a typical day, can be used to deliver a lower BER, or a higher data rate, or both. In the case of concern to us, if BER is fixed, data rate on a good day could be, for some locations, four or more times higher than the worst-case rate. It is inefficient to waste this “margin”. Note that the system is steered - it does need to handle the globally worst weather each day

• However, the process of adjusting the data rate - and the whole broadcast schedule that goes with it - takes time. A good estimate of the usable data rate needs to be made 6 to 12 hours ahead of time

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Research Problems

• We would like to develop, 6 to 12 hours ahead of time, global maps of achievable data rates

• Because we are concerned with broadcast (one-way) systems, we cannot know in real-time if the data rates selected turn out to be too high (and the resulting BER too high), so we need information on the variability in the weather fields used, to compute the variability in the resulting attenuation forecasts, and account for it by increasing the forecast attenuation beyond the expected value

• But the field of most impact on us, rainfall rate, varies most rapidly in space and time

• Rainrate fields available to us include both NWP data, and new data which uses long revisit-time microwave imagers to calibrate short-revisit time infrared imagery, giving derived (and delayed) observational data

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Research Problems (2)

• The ITU attenuation models used, are tuned to produce good results with climatological data. What risks do we run feeding them with forecast data?

• Weather radars offer insight into rain rate and cloud water content in real time. We have beacon receivers collocated with some radars, so we can begin to model attenuation at some grid points. How do we combine these real-time, tuned models, with the older models?

• Bottom line question: Is there so much variability in the forecast fields we need that we’d be better off sticking with climatology for all of them? How about some of them (like rainrate)? Or can we combine NWP forecast rainrate data, quasi-observational rainrate data, and climatology, to get a better answer?

04/19/23 14

Research Hierarchy: Forecast Data

Link Budget

Weighted loss sum

Rain loss

(DAH)

Cloud loss

(ITU 840)

Gas loss(ITU 676)

G/T

Rainfall rate:

-NOGAPS*

- Calibrated IR Imagery

Cloud H20 content

NOGAPS

Air tempSurface Pressure

Specific HumidityFreezing Layer Height

NOGAPS

O2 Loss H20 vapor loss

676 H2O loss

*Navy Operational Global Atmospheric Prediction System

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Research Data Source: NOGAPSNavy Operational Global Atmospheric Prediction System

•Basic equation: Primitive equations with hydrostatic approximation

•Integration domain: Global, surface to 1 mb

•Horizontal resolution: T159 (~0.75 degree on the Gaussian grid). Output at 1 degree (81 km).

•Vertical levels: 5 sigma levels below 850 mb, depending on terrain elevation

•Forecast time: 144 hrs

•Initial fields: Machenhauer initialization from the +/- 3 hour cut-off Optimum Interpolation Analysis

•First-guess fields: Previous NOGAPS 6-h or 12-h forecast

•Moisture physics: Convective precipitation (relaxed Arakawa-Schubert), shallow cumulus mixing (Tiedtke) and large-scale convection.

•Ocean surface: Sea surface temperature from U. S. Navy's Ocean Thermal Interpolation System (OTIS 4.0) and ice coverage percentage from the NAVICE Center weekly analysis.

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References

• International Telecommunications Union (ITU), Radiocommunication Bureau, “Handbook of Radiometeorology”, Geneva, 1996

• ITU, “Handbook: Radiowave Propagation Information for Predictions for Earth-to-Space Path Communications,” Geneva, 1996

• ITU Report 563-4, “Radiometeorological Data”, Geneva, 1990, p. 123• ITU Recommendation ITU-R P.618-5, “Propagation Data and Prediction

Methods Required for the Design of Earth-Space Telecommunication Systems”, Geneva, 1997

• ITU Recommendation ITU-R P.676-3, “Attenuation by Atmospheric Gases”, Geneva, 1997

• ITU Recommendation ITU-R P.836-1, “Water Vapour: Surface Density and Total Columnar Content”, Geneva, 1997

• ITU Recommendation ITU-R P.840-2, “Attenuation Due To Clouds and Fog”, Geneva, 1997

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References (2)

• ITU Recommendation ITU-R P.837-1, “Characteristics of Precipitation for Propagation Modeling”, Geneva, 1994

• ITU Recommendation ITU-R P.841, “Conversion of Annual Statistics to Worst-Month Statistics”, Geneva, 1992

• Dissanayake, A., Allnutt, J., and Haidara, H., “A Prediction Model that Combines Rain Attenuation and Other Propagation Impairments Along Earth-satellite Paths”, IEEE Trans. On Antennas and Propagation, Vol. 45. No. 10, October 1997

• Rice., P. and Holmberg, N., “Cumulative Time Statistics of Surface-Point Rainfall Rates”, IEEE Trans. On Communications, Vol. COM-21, No. 10, October 1973

• Fitzgerald, G. and Bostrom, G, “GBS Data Mapper: Modeling Worldwide Availability of Ka-Band Links Using ITU Weather Data”, 6th Ka-Band Utilization Conference, Cleveland OH, June 2000