RF Propagation Characteristics on Leg 1 of July 06 OC3570 Cruise: Comparison of cruise sounding data, climatology and model data.

Similar documents
NAVAL POSTGRADUATE SCHOOL Monterey, California THESIS VARIABILITY OF REFRACTIVITY IN THE SURFACE LAYER. Deborah L. Mabey

The project that I originally selected to research for the OC 3570 course was based on

Improved EM Tactical Applications through UAS-Enhanced High-Resolution Mesoscale Data Assimilation and Modeling

Near-surface Measurements In Support of Electromagnetic Wave Propagation Study

I. Objectives Describe vertical profiles of pressure in the atmosphere and ocean. Compare and contrast them.

6.3 SEA BREEZE CIRCULATIONS INFLUENCING RADIO FREQUENCY SYSTEM PERFORMANCE AROUND CALIFORNIA BAJA SUR

Determination of Cloud Bottom Height from Rawinsonde Data. Lt Martin Densham RN 29 August 05

VAISALA RS92 RADIOSONDES OFFER A HIGH LEVEL OF GPS PERFORMANCE WITH A RELIABLE TELEMETRY LINK

BUFR Table D List of common sequences

Evaporation Duct Height Climatology for Norwegian Waters Using Hindcast Data

William H. Bauman III * NASA Applied Meteorology Unit / ENSCO, Inc. / Cape Canaveral Air Force Station, Florida

Refractivity Data Fusion

Introduction to upper air measurements with radiosondes and other in situ observing systems. John Nash, C. Gaffard,R. Smout and M.

Comparison of Vaisala Radiosondes RS41 and RS92 WHITE PAPER

PRESENTATIONS ON RECENT NATIONAL TESTS/COMPARISONS. Recent Tests and Comparisons of Radiosonde Operated by Japan Meteorological Agency

Sami Alhumaidi, Ph.D. Prince Sultan Advanced Technology Institute King Saud University Radar Symposium, Riyadh December 9, 2014

The California current is the eastern boundary current that lies to the west of

BUFR Table D - List of common sequences

Met Office Intercomparison of Vaisala RS92 and RS41 Radiosondes

The Relationship Between Atmospheric Boundary Layer Structure and Refractivity

SHEBA GLASS SOUNDING DATA

Instrument Cross-Comparisons and Automated Quality Control of Atmospheric Radiation Measurement Data

The Vaisala AUTOSONDE AS41 OPERATIONAL EFFICIENCY AND RELIABILITY TO A TOTALLY NEW LEVEL.

Government of Sultanate of Oman Public Authority of Civil Aviation Directorate General of Meteorology. National Report To

High accuracy. Proven reliability. / SOUNDING SOLUTIONS

Observing Weather: Making the Invisible Visible. Dr. Michael J. Passow

Introduction to the College of DuPage NEXLAB Website

EAS 535 Laboratory Exercise Weather Station Setup and Verification

A Case Study on Diurnal Boundary Layer Evolution

Studying Topography, Orographic Rainfall, and Ecosystems (STORE)

Performance of Radar Wind Profilers, Radiosondes, and Surface Flux Stations at the Southern Great Plains (SGP) Cloud and Radiation Testbed (CART) Site

R.C.BHATIA, P.N. Khanna and Sant Prasad India Meteorological Department, Lodi Road, New Delhi ABSTRACT

NAVAL POSTGRADUATE SCHOOL THESIS

IOP-2 Summary of Operations 03 June UTC 04 June UTC Authors: Market, Kastman

Applications of Meteorological Tower Data at Kennedy Space Center

CHAPTER 12. MEASUREMENT OF UPPER-AIR PRESSURE, TEMPERATURE AND HUMIDITY

The Payerne Meteolabor Radiosonde

M. Mielke et al. C5816

VALIDATION OF CROSS-TRACK INFRARED SOUNDER (CRIS) PROFILES OVER EASTERN VIRGINIA. Author: Jonathan Geasey, Hampton University

Forecasting of Optical Turbulence in Support of Realtime Optical Imaging and Communication Systems

NAVAL POSTGRADUATE SCHOOL THESIS

The Meisei sonde data product

Complex Terrain (EDUCT) experiment, conducted by the National Center for Atmospheric

Guided Notes Weather. Part 1: Weather Factors Temperature Humidity Air Pressure Winds Station Models

Christian Sutton. Microwave Water Radiometer measurements of tropospheric moisture. ATOC 5235 Remote Sensing Spring 2003

Climate & Earth System Science. Introduction to Meteorology & Climate. Chapter 05 SOME OBSERVING INSTRUMENTS. Instrument Enclosure.

Geodetics measurements within the scope of current and future perspectives of GNSS-Reflectometry and GNSS-Radio Occultation

Module 11: Meteorology Topic 3 Content: Weather Instruments Notes

Α neural network based prediction method for troposheric ducting over the Hellenic region

NRL Four-dimensional Variational Radar Data Assimilation for Improved Near-term and Short-term Storm Prediction

The Vaisala Reference Radiosonde Program: First Results and Future Plans

MEASUREMENTS AND MODELLING OF WATER VAPOUR SPECTROSCOPY IN TROPICAL AND SUB-ARCTIC ATMOSPHERES.

This wind energy forecasting capability relies on an automated, desktop PC-based system which uses the Eta forecast model as the primary input.

A sonde monitoring and display facility for DYNAMO P. Ciesielski and R. Johnson

MODEL TYPE (Adapted from COMET online NWP modules) 1. Introduction

The Causes of Weather

WMO INTERNATIONAL RADIOSONDE COMPARISON

IMPACT OF GROUND-BASED GPS PRECIPITABLE WATER VAPOR AND COSMIC GPS REFRACTIVITY PROFILE ON HURRICANE DEAN FORECAST. (a) (b) (c)

Construction and Interpretation of Weather Station Models

NAVAL POSTGRADUATE SCHOOL THESIS

Long Term Autonomous Ocean Remote Sensing Utilizing the Wave Glider

Correction for Dry Bias in Vaisala Radiosonde RH Data

Refractivity-from-Clutter

DEPARTMENT OF EARTH & CLIMATE SCIENCES Name SAN FRANCISCO STATE UNIVERSITY Nov 29, ERTH 360 Test #2 200 pts

13B.2 COMPARISON OF SELECTED IN-SITU AND REMOTE SENSING TECHNOLOGIES FOR ATMOSPHERIC HUMIDITY MEASUREMENT

WM9280. Pro Family weather station with T/H sensor, pluviometer, anemometer, PC connection and Meteotime weather forecasts until 3 days

For the operational forecaster one important precondition for the diagnosis and prediction of

CHAPTER 13 WEATHER ANALYSIS AND FORECASTING MULTIPLE CHOICE QUESTIONS

The Behaviour of the Atmosphere

P2.6 EVALUATION OF THE WVSS-II MOISTURE SENSOR USING CO-LOCATED IN-SITU AND REMOTELY SENSED OBSERVATIONS

1 Executive summary. 2 Principles of SAT-OCEAN service

Regional Climatology. Lab Number One Atmospheric Processes

Sensitivity of Convective Indices to Humidity Adjustments

4B.4 STATISTICAL SHORT-RANGE GUIDANCE FOR PEAK WIND SPEEDS AT EDWARDS AIR FORCE BASE, CA

Discoverer Automated Weather System Data Quality Control Report

Unseasonable weather conditions in Japan in August 2014

MAST ACADEMY OUTREACH. WOW (Weather on Wheels)

4 Forecasting Weather

SOME STEP OF QUALITY CONTROL OF UPPER-AIR NETWORK DATA IN CHINA. Zhiqiang Zhao

Visibility in Low Clouds And Its Impact on FSO Links

4 Forecasting Weather

Overview of Met Office Intercomparison of Vaisala RS92 and RS41 Radiosondes

Ten years analysis of Tropospheric refractivity variations

Use of Data Logger Devices in the Collection of Site- Specific Weather Information. Peter de Bruijn, RPF. June Fire Report FR

Observations of Integrated Water Vapor and Cloud Liquid Water at SHEBA. James Liljegren

The lesson essential questions that will guide our investigations are:

DYNAMO/CINDY Sounding Network

The first tropospheric wind profiler observations of a severe typhoon over a coastal area in South China

RECENT WORLDWIDE GPS RADIOSONDE PERFORMANCE Incorporating the review of WMO GPS questionnaire, 2001

LIST OF AMENDMENTS TO MANUAL ON CODES

Specifications for a Reference Radiosonde for the GCOS Reference. Upper-Air Network (GRUAN)

Investigation of the Arizona Severe Weather Event of August 8 th, 1997

9.3 AIR-SEA INTERACTION PROCESSES OBSERVED FROM BUOY AND PROPAGATION MEASUREMENTS DURING THE RED EXPERIMENT. 2.1 The NPS Buoy

Website Phone Mobile OVERVIEW Davis Vantage Pro2 Weather Station

P1.12 MESOSCALE VARIATIONAL ASSIMILATION OF PROFILING RADIOMETER DATA. Thomas Nehrkorn and Christopher Grassotti *

Ship-Based UAV Measurements of Air-Sea Interaction in Marine Atmospheric Boundary Layer Processes in the Equatorial Indian Ocean

Quality assurance for sensors at the Deutscher Wetterdienst (DWD)

Efficacy Evaluation of Data Assimilation for Simulation Method of Spilled Oil Drifting

Activity: The Atmosphere in the Vertical

Tropical Cyclone Formation/Structure/Motion Studies

Transcription:

RF Propagation Characteristics on Leg 1 of July 06 OC3570 Cruise: Comparison of cruise sounding data, climatology and model data. LCDR Bob Jones

Introduction and Background The purpose of this project was to collect upper air sounding data at various stations during the first leg of the July 2006 cruise, and then use this data to determine the radio frequency (RF) propagation characteristics. Of particular interest is the identification of trapping layers and surface and elevated ducts. This data is then compared to upper air climatology data, Eta (eta vertical coordinate system), Global Forecasting System (GFS), and Navy Operational Global Atmospheric Prediction System (NOGAPS) upper air model data. Variables of pressure, temperature, and humidity (vapor pressure) are used to determine a dimensionless refractivity index N, valid for very high frequency (VHF) and ultra high frequency (UHF) communications, where: K p K e 2 K 5 e 2 N = 77.6 5.6 + 3.75 10 hpa T hpa T hpa T p = Atmospheric Pressure (hpa) T = Temperature (K) e = Vapor Pressure (hpa) This value is then converted to a modified refractive index M useful for determining the presence of ducts, where: ( ) 1 M = Nz + 157km z z = Height (km) Using a plot of M versus height, the gradient of M (dm/dz) can be calculated and used to determine the presence of trapping layers and ducts. Trapping layers occur where dm/dz is negative which is created by a negative humidity gradient and/or a positive temperature gradient. The top of a duct will be at the top of the trapping layer, but the bottom of a duct can extend down to the surface (surface duct), the M value lower in the atmosphere equal to the M value at the top of the trapping layer (elevated duct), or a combination of both (see figure 1). It is

common over the ocean to have a negative M gradient at the surface (strong negative humidity gradient) which creates what is called an evaporative duct (also illustrated in figure 1). Figure 1) Trapping layers and ducts: (a.) Surface trapping layer and evaporative duct, (b.) Elevated trapping layer with surface based duct, (c.) Elevated trapping layer and elevated duct, (d.) Multiple trapping layers with both surface and elevated ducts. At the top of the trapping layer and duct, electromagnetic (EM) energy in the communication bands (VHF/UHF 30MHz to 3 GHz) is directed downward with a curvature greater than that of the earth. At the bottom of the duct, the EM energy is directed upward. This process continues in the duct, propagating the EM energy great distances (beyond that normally expected). It is also noteworthy to point out that the ability of EM energy to travel in the duct is

dependent on the frequency of the energy and the thickness of the duct (as duct thickness increases, lower frequency energy is more readily trapped in the duct). Data and Methods The observed upper air data used in this study was collected from 19 July 06 to 22 July 06 during the first leg of the summer 2006 OC3570 cruise on the R/V Point Sur. Sixteen radiosondes were used during the first leg of the cruise (sondes 1-10 and 12-16 by weather balloon and sonde 11 on a kite flown by Dr. Peter Guest). Sondes 1, 6, 7, 15, and 16 were full atmosphere soundings. The remaining sondes were up/down profiles, where a small leak was introduced into the weather balloon, allowing partial atmospheric ascent, followed by descent of the radiosonde under a parachute. The radiosondes used were the Vaisala RS80-15L. This radiosonde uses a capacitive aneroid housed within a capsule for barometric pressure measurements, a capacitive bead temperature sensor, a capacitive thin film humidity sensor, and a solid state electronic switch connecting all sensors to the transducer electronics allowing all parameters to be measured at approximately 1.5 second intervals. The RS80-15L is powered by a water activated, lightweight 19 volt battery, and uses the Loran-C navigation network for determining wind direction and speed. The RS80-15L radiosondes transmitted raw sounding data to the R/V Point Sur at a nominal frequency of 403 MHz, with a tuning range of 400 to 406 MHz. The receiver equipment used on the R/V Point Sur was the Vaisala DigiCORA Sounding System MW31 (receiver and Windows based PC software). Raw sounding data was then converted to text file format for each sounding location. To ensure raw data could be ingested and processed by the Advanced Refractive Effects Propagation System (AREPS) software version 3.6.02.21 (limited to decreasing pressure with height), sounding data was inspected for missing data (specifically pressure, air and dewpoint

temperatures, and humidity data), as well as very rapid ascent or descent rates. These lines as well as radiosonde descent data at the end of the sounding were removed from the data. Sonde 12 data was found to be missing all dewpoint temperature and humidity data, and was therefore not used in the study. Data from the sonde (11) tethered to a kite was also not used as it looked at a very shallow layer of the atmosphere, and was continuously in an ascent and descent profile. Environments and M-unit refractive profiles for each of the remaining fourteen radiosonde data files were created using the environment creation and propagation condition summary features of AREPS. An M-unit profile was then created using July climatological data from the Oakland, California station (closest sounding station to the operating area). As it seemed unreasonable that climate data from one station would provide much meaningful comparison with the actual upper air data, it was decided that archived numerical model data from the cruise time period would also be used for comparison. The models available for use were the regional Eta model (box over Monterey and San Francisco Bays out to approximately 37.00 degrees W), and the global GFS and NOGAPS models. Data from the models were taken at or as close as possible to the actual sounding times, and on the precise location of the sounding launches (no account for the drift associated with a balloon borne radiosonde). The GEMPAK (General Meteorology Package) Analysis and Rendering Program (GARP) was used to convert model data into upper air sounding profiles for each model at each location. The soundings were converted into text format, saved, and ingested into AREPS using the same procedure to create environments and M-unit refractive profiles as was used for the raw sounding data. During this process it was found that some of the upper level (200 to 50 hpa) GFS data was missing, and more importantly, only six of the cruise

sounding stations (1-4, 7, and 16) were located in the domain of the regional Eta model data. For this reason, only these six stations are discussed in this study (see figure 2 for locations). For better resolution of the lower atmospheric M-unit refractive profiles, height was limited to 4100 meters in full atmosphere profiles (locations 1, 7, and 16), and to the maximum height of the actual sounding in the remaining profiles (locations 2-4). Figure 2) July 2006 cruise chart. Red open circles and labels denote radiosonde deployment locations for the six sets of sounding data used in this study. Results Climatology A July climatological M-unit profile for Oakland, California shows an average surface duct up to 130 meters and a 235 meter thick elevated duct from 415 meters to 650 meters (figure

3). During July the average percentage of time that both elevated and surface ducts occur together is 1.9 %. Surface-based ducts occur approximately 3 % during the night and 27 % during the day, and elevated ducts tend to occur 77 % of night time and 36 percent of the day (figures 4 and 5). Figure 3) AREPS created M-unit refractive profile showing average heights of surface and elevated ducts during July. Profile is based on climatological upper air sounding data at the Oakland, California sounding station. Figure 4) Percent day and night occurrence of surface-based ducts (bold red arrow at July). Bar chart is based on climatological upper air sounding data at the Oakland, California sounding station.

Figure 5) Percent day and night occurrence of elevated ducts (bold red arrow at July). Bar chart is based on climatological upper air sounding data at the Oakland, California sounding station. Sounding and model data For simplicity, sounding launch times and model run times will be in the following format: YYMMDDHHMMZ YY Numerical year MM Numerical month DD Numerical day HH Hour MM Minutes Z Indicates zulu time (Greenwich Mean Time) Sounding station 1 Sounding 1 was launched on 0607191833Z (1133 local) at 36.7303N 122.0240W. Eta, GFS, and NOGAPS model data is from the 0607191200Z model run, 6 hour forecast. Figure 6 shows M-unit refractive profiles created in AREPS. Actual sounding data indicates the presence of a surface based duct up to 17 meters and an elevated duct from approximately 270 meters to 550 meters. As can be seen, none of the models predict any ducting at this location. The Eta profile is very close to the actual sounding M-unit values.

Figure 6) M-unit refractive profiles created in AREPS for sounding station 1: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Sounding station 2 Sounding 2 was launched on 0607192029Z (1329 local) at 36.7167N 122.2360W. Eta, GFS, and NOGAPS model data is again from the 0607191200Z model run, 6 hour forecast. Figure 7 shows M-unit refractive profiles created in AREPS. Sounding data shows a weak surface based duct up to 7 meters and significant elevated ducting from 135 meters to 360 meters. Eta model data shows a thick elevated duct from 120 meters to 680 meters, but no surface based duct. GFS model data again is not close to the sounding, however NOGAPS data has very close M-unit values and similar shaped profile, but does not predict any ducting (only super-refraction). Sounding station 3 Sounding 3 was launched on 0607192351Z (1651 local) at 36.6017N 122.5070W. Eta, GFS, and NOGAPS model data is from the 0607200000Z model run, analysis. Figure 8 shows M-unit refractive profiles created in AREPS. Sounding data shows a surface based duct to 15 meters and the base of an elevated duct at 90 meters. The top of the elevated duct is difficult to determine due to the large amount of noise in the profile. Eta model data shows a surface based duct extending up to approximately 440 meters, while the GFS and NOGAPS models do not predict any ducting. Sounding station 4 Sounding 4 was launched on 0607200450Z (2150 local on the 19th) at 36.3768N 122.9590W. Eta, GFS, and NOGAPS model data is from the 0607200000Z model run, 6 hour forecast. Figure 9 shows M-unit refractive profiles created in AREPS. Sounding data shows a surface based duct up to 15 meters and an elevated duct from 210 meters to 420 meters. Again the data above this is difficult to decipher due to the large amount of noise in the profile. The

Eta model profile shows a surface based duct up to approximately 440 meters. Again GFS and NOGAPS profiles do not agree with actual sounding data. Sounding station 7 Sounding 7 was launched on 0607202339Z (1639 local) at 36.8877N 123.0900W. Eta, GFS, and NOGAPS model data is from the 0607210000Z model run, analysis. Figure 10 shows M-unit refractive profiles created in AREPS. Sounding data shows a shallow surface based duct up to 10 meters and an elevated duct from 21 meters to approximately 490 meters (to most platforms this would probably appear as deep continuous surface based duct). The Eta model shows a surface based duct to approximately 450 meters with M-unit values close to that of the actual sounding. As before GFS and NOGAPS model data predicts no ducting, with no comparable M-unit values. Sounding station 16 Sounding 16 was launched on 0607222259Z (1559 local) at 37.6614N 122.8281W (outside the San Francisco Bay as shown in figure 2). Eta, GFS, and NOGAPS model data is from the 0607230000Z model run, analysis. Figure 11 shows M-unit refractive profiles created in AREPS. Sounding data shows a surface based duct up to 10 meters and an elevated duct from 25 meters to 280 meters (as with station 7, this would probably appear as a thick surface duct to most platforms). Eta model data shows a surface based duct to 230 meters with a stronger negative M-unit gradient than the actual sounding shows. GFS model data again predicts no ducting, but NOGAPS model data predicts a surface based duct to 460 meters with a similar M- unit negative gradient as shown in the actual sounding.

Figure 7) M-unit refractive profiles created in AREPS for sounding station 2: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Figure 8) M-unit refractive profiles created in AREPS for sounding station 3: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Figure 9) M-unit refractive profiles created in AREPS for sounding station 4: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Figure 10) M-unit refractive profiles created in AREPS for sounding station 7: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Figure 11) M-unit refractive profiles created in AREPS for sounding station 16: (a) Actual sounding data, (b) Eta numerical model sounding data, (c) GFS numerical model data, and (d) NOGAPS numerical model data.

Discussion In contrast to what the Oakland climatology data shows, surface based ducts were seen in all actual soundings. As discussed in the introduction, a surface based evaporation duct should be present over the ocean almost continuously due to the rapid decrease of humidity above the water surface. The evaporation duct was seen to decrease in thickness during the day and increase in thickness at night. Compared to a deep surface based duct we would expect only high frequency trapping in the shallow evaporation ducts. Elevated ducts appeared more frequently and at higher levels at night with more deep surface ducts appearing during the day. This is in general agreement with the surface and elevated duct day/night percentage trends shown by climatology. At all six locations presented in this study, the global model data performed poorly to what was actually observed, with GFS being the worst. This was also the case with the locations not discussed. One possible explanation for this is incomplete model data for the locations selected. It was noted on several occasions, particularly with GFS, that data was missing below 1000 hpa and in some cases below 950 hpa resulting in a large dew point depression and low relative humidity at the lowest layer. One possible solution could be to select an area instead of a single point. The Eta regional model performed relatively well, although never predicted an actual evaporative duct. The biggest problem with the Eta model is the size of the domain used. This limited regional model studies to just a few sounding locations, with none during the day on 21 July, when the ship was in very low overcast conditions and reduced visibility. It also resulted in all studies being located on the eastern side of the California Current and therefore all in similar

seawater properties. One other possible complication with the Eta model is that most of the sounding data was taken on the edge of the domain, where model accuracy is lower. While AREPS is an extremely useful tool, the ability to accurately predict RF propagation conditions ultimately comes down to the ability to accurately predict the environment. This becomes operationally significant at communication frequencies, not only for being able to communicate with friendly units, but probably more important to prevent detection of ownship transmissions by unfriendly units (and being able to detect enemy transmissions). Conclusion This study has shown the utility of regional numerical models (specifically Eta) in providing forecast upper air data for prediction of RF propagation conditions. Climatology, while a good starting point, does not provide the detail needed in day to day fleet operations. Global model data appears to be too coarse to provide accuracy for small scale RF propagation. Further studies and research Prior to the next cruise a larger regional model domain or one covering the entire operating area should be requested. In addition Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model data should be requested as well so comparisons can be made between different regional models. The possibility of Distributed Atmospheric Mesoscale Prediction System (DAMPS) runs should also be explored as this will give even finer resolution. RF propagation measurements should be made to determine the accuracy of AREPS. This would require coordination with some shore facility or another vessel and may be beyond the scope and ability of the OC3570 cruise.

If the California Current is found close to shore on subsequent deployments, soundings and regional model data should be collected on both sides to examine the differences in RF propagation characteristics over the different water masses. One final note is on the amount of noise in the sounding data. It was discussed and attempts were made to delete some of the sounding data to smooth out the M-unit profiles. However, it became apparent that the data was being modified to fit a desired profile and not necessarily an accurate profile. For this reason all data was processed resulting in the noisy profiles. Acknowledgments Thanks to the crew of the R/V Point Sur and personnel at the Moss Landing Marine Laboratories. Thanks to Dr. Peter Guest for providing expertise on sounding equipment operation, data conversion, AREPS instruction, and answers to what seemed to be never ending last minute questions. Thanks to Professor Bob Creasey for help with obtaining model data and GARP instruction, and to Professor Mary Jordan for pointing me in the right direction. Thanks also to Dr. Ken Davidson for his EM/EO instruction and excellent class notes. Finally thanks to Vaisala (www.vaisala.com) for brochures on their radiosondes and DigiCORA sounding systems.