Visibility in Low Clouds And Its Impact on FSO Links

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1 Visibility in Low Clouds And Its Impact on FSO Links M. Ammar Al-Habash, Janae Nash, Jeff Baars, Michael Witiw, Ken Fischer, Ken Desmet Terabeam Corporation, NE 87th St., Building C, Redmond, WA ABSTRACT Fog and low clouds are the two atmospheric elements with the greatest impact on the performance of a free space optical (FSO) network. Predicting the effects of low clouds and ground based fog on FSO equipment performance is a challenging exercise. Usually, surface visibility records from airports in proximity to the deployment area are used to calculate the link availability. However, very little data are available on visibility within clouds, which have a larger impact on elevated links. To estimate the visibility in low clouds we have deployed visibility sensors at three different heights (33, 119, 188 meters above mean sea level) and a ceilometer in San Francisco from June to October of The data collected show substantial difference between the visibility reported at San Francisco International Airport (SFO) and the visibility recorded by our sensors in downtown San Francisco. More importantly, the data indicate a great er prevalence of low clouds downtown than at the airport. 1. INTRODUCTION Fog and low clouds are the atmospheric phenomena with the greatest impact on FSO link budgets and availabilities. 1 To determine accurate link availabilities, the microclimate of a given deployment site must be understood. The actual calculation of the availability is typically based on surface visibility data collected at airports near the deployment location. In using the airport data, we usually assume that it is representative of the location of interest or make adjustments to the data. Although airports routinely gather cloud data, little is known about the visibility within the clouds themselves. Therefore, it is difficult to estimate the visibility at different heights when low clouds exist. This makes calculating the link availability at different heights a cumbersome process. In order to construct accurate models to determine link availability, an understanding of visibility within clouds is imperative. In the spring of 2001 Terabeam deployed several weather instruments in San Francisco to study fog and low clouds. The instruments include three visibility sensors, one ceilometer, and a weather station. The goals of this weather network are threefold: 1) to understand the fog and low cloud formation mechanism in San Francisco, 2) to explore the properties of fog and low clouds and their impact on visibility, 3) to compare data measured by our weather network to the data reported by San Francisco International Airport. San Francisco is renowned for its foggy weather and hilly terrain. Summer weather is characterized by little rain and cool, marine air that often results in coastal stratus and low clouds in the city. San Francisco s winter weather is characterized by many sunny days. However periodic rain-induced fog, occasional sea fog and radiation fog result in a wintertime climate that often exhibits visibilities and cloud ceilings much lower than those observed in the summer. The high occurrence of low clouds and fog and the two distinctive fog seasons make San Francisco an ideal city for a low cloud and fog study. The hills of the city also prove advantageous, as the visibility sensors can be placed at different elevations to study the visibility within clouds while still keeping the sensors within blocks of one another. 1.1 Weather network site locations Our instruments were deployed at three sites located in the northeastern part of the San Francisco peninsula. Our lowest station, Site 1, is equipped with a Vaisala CT25K Ceilometer, a Qualimetrics 8364-E Visibility Sensor, and a weather station that includes an HMP45C temperature and relative humidity probe, an R.M. Young Anemometer, a TE525 Tipping Bucket Rain Gauge, a LI200X Pyranometer, and a CS105 Pressure Gauge. Our other two stations, Site 2 and Site 3, are at an elevation of 119 and 188 meters MSL respectively. They are both equipped with a Belfort 6100 Visibility Sensor that measures visibility and extinction coefficient.

2 Site 1 Nob Hill/ Site 3 Site 2 SFO Figure 1: Topographical view of San Francisco and the visibility sensor sites, with 4x terrain exaggeration (on the right ). 2. DATA COLLECTED All sensors were deployed in San Francisco from June 1, 2001 to October 27, This time period primarily captures the summer low cloud season in San Francisco. 2.1 Wind data Wind direction and speed play an important role in studying fog and low clouds and in resolving fog type. The complexity of San Francisco s topography causes significant local variations in the wind. Due to the East Pacific High in place during the summer, onshore winds are the most frequently observed along the California coast. At low levels, the wind blows in from the ocean and is channeled around the hills. As seen if Figure 2, the winds at Site 1 are most likely channeled around the south of Nob Hill and flow towards the northeast. This flow pattern is consistently seen in the data. Nearly 40% of the winds throughout our experiment period were southwesterly. Over 75% of the winds measured from June 1 to October 27, 2001 were from the third quadrant. The wind direction distribution was also determined for cases when the ceiling was less than 200 meters. It is not surprising that a similar distribution was found - in fact, 45% of the wind directions from this set are southwesterly, and over 85% of the wind directions are between south-southwesterly and west-southwesterly, inclusive. Southwesterly wind is the expected wind direction during advection fog events in San Francisco. A southwesterly wind direction measured at Site 1 is most likely to occur when the wind is coming from the Pacific Ocean. Because sea fog and stratus form over San Francisco s cool Pacific coastal waters 2 and our data shows that southwesterly wind was the dominate wind direction, it is safe to assume that sea fog and stratus are the primary causes of San Francisco s summertime low clouds.

3 Figure 2: Wind direction and speed distribution measured throughout the summer season. 2.2 Ceiling Data Surface visibility is an essential parameter in the link availability and may suffice in some locations. Because tall buildings are good candidates for FSO network hub sites due to their wide view angle, low cloud ceilings are an important issue especially in locations where cloud ceilings often occur near or below the heights of buildings. Figure 3 shows the probability density of occurrence of ceiling heights between 0 and 1000 meters. The data seems to fit the log-normal probability density function (with µ=5.46, s=0.53) better than the gamma PDF (with a=2.99, ß=91.40, calculated from the mean and the standard deviation). shows the probability that ceiling occur below particular heights. San Francisco s tallest building is 298 meters; according to Table 3 nearly 27% of the time the top of this building would be at or very near the base of the cloud, thus experience reduced visibility. In general, frequent occurrences of low clouds in San Francisco make tall building less attractable for rooftop installation. Ceiling Level < 500 meters 34.78% < 400 m 32.64% < 300 m 26.99% < 200 m 17.54% < 175 m 13.45% < 150 m 9.46% < 125 m 7.46% < 100 m 3.14% < 75 m 0.39% Percentage Occurrence < 50 m 3.9x10-4 % Figure 3: The distribution of the ceiling height fitted to a gamma (a=2.99, ß=91.40) and log-normal (µ=5.46, s=0.53) probability density functions (left). Table 1: Ceiling height probabilities (right).

4 2.3 Visibility Data Table 2 shows occurrences of visibility below given distances at our three stations. It is clear that as the height of the station increases the percentage of low visibility also increases. It is significant that the Site 3 had the most missing data (due to power supply failure) yet still observed the most minutes of low visibility. Vis. < 1000 m Vis. < 500 m Vis. < 200 m Vis. < 100 m Site 1 (33 m) 0.12% 7.46x10-5 % 0 0 Site 2 (119 m) 5.03% 2.59% 0.19% 1.4x10-5 % Site 3 (188 m) 13.22% 10.13% 2.75% 9.8x10-5 % Table 2: Percentage of time each station reported low visibilities. For each ceiling height, the visibility Sites 2 and 3 is plotted. The results are shown in Figure 4 for Site 2 station, Site 3 results (not shown) are similar. It seems that the log-normal distribution fits the data best when the ceiling height is lower than the height of the station. However, as the ceiling height approaches the station height the visibility distribution seems to approach uniform distribution. It is also seen that the distribution mode shifts towards higher visibilities as the cloud ceiling height increases. (a) (c) (b) (d)

5 (e) (f) Figure 4 a-f: The visibility distribution at the Site 2 station when ceiling was reported at a) 60 meters, b) 76 meters, c) 91 meters, d) 106 meters, e) 121 meters, f) 137 meters (AMSL). Table 3 and Table 4 below show the mean and standard deviation of visibility for each ceiling height. Notice that both the mean and standard deviation of visibility increase with increasing ceiling height in all cases. Because ground-based fog or clouds below 100 meters are rare in the summer, the visibility data from Site 1 are not presented. Ceiling 61 m 76 m 91 m 106 m 121 m 137 m 152 m 167 m 182 m Mean 199 m Visibility Standard Deviation Ratio N Table 3: Visibility during low cloud events at Site 3. Ratio represents the percentage of events in which the visibility dropped below 1000 meters for a particular ceiling. N is the number of minutes of a particular ceiling height. Ceiling 61 m 76 m 91 m 106 m 121 m Mean 332 m Visibility Standard Deviation Ratio N Table 4: Visibility during low cloud events at the Site 2. Ratio represents the percentage of events in which the visibility dropped below 1000 meters for a particular ceiling. N is the number of minutes of a particular ceiling height.

6 3. COMPARISON TO AIRPORT DATA It is often assumed that within a reasonable distance the data collected at the airport is applicable to the city of interest. To ensure this correlation, a detailed local micro-climatological analysis of the area must be performed. San Francisco is a good example of the deviation between measurements at the airport and measurements from the downtown area. The total hours where the ceiling was below 1000 meters are shown in Figure 5-Figure 9 for each month. Also in these figures, are the visibility obtained from airport data and the visibility recorded by our three sensors for each month. The minute by minute temporal correlation coefficient of ceiling heights is also depicted in Table 5 below. Month June July August September October Corr. Coeff Table 5: The monthly ceiling height correlation coefficient based on minutely data. Figure 5: The ceiling heights recorded at Site 1 and the airport (left) and the visibility as recorded at the airport and by our three sensors (right) during the month of June. Figure 6: The ceiling heights recorded at Site 1 and the airport (left) and the visibility as recorded at the airport and by our three sensors (right) during the month of July.

7 Figure 7: The ceiling heights recorded at Site 1 and the airport (left) and the visibility as recorded at the airport and by our three sensors (right) during the month of August. Figure 8: The ceiling heights recorded at Site 1 and the airport (left ) and the visibility as recorded at the airport and by our three sensors (right) during the month of September. Figure 9: The ceiling heights recorded at Site 1 and the airport (left) and the visibility as recorded at the airport and by our three sensors (right) during the month of October.

8 It is evident from the plots above that ceiling height figures above that low clouds occur downtown in much higher frequency apparently due to closer proximity to the oceanic source of the low clouds and fewer hills blocking their advection into the downtown area. 4-TRAJECTORY STUDY Following Goodman 3, air trajectories were calculated and analyzed for low cloud events that occurred during the study period. Goodman found slight microphysical differences between fog events that formed from air flowing over land versus those from air flowing over water. Figure 10: Backward trajectory ending 05 UTC, 13 th July. Figure 12: Backward trajectory ending 05 UTC, 13 th July. Figure 11: Backward trajectory ending 05 UTC, 13 th July. Figure 13: Backward trajectory ending 05 UTC, 13 th July.

9 Backwards trajectories were calculated using the HYbrid Single-Particle Lagrangian Integrated Trajectory 4 model for each of 53 low cloud cases occurring during the experiment. A case was defined as any time cloud ceilings dropped below the height of Site 3 visibility sensor for a period of more than 30 minutes. It was suspected that cloud events that formed from air having a trajectory over land might have differing (lower) visibilities from those having trajectories over the ocean. Each of the cases was then categorized into one of four categories: marine, marine/continental, continental/marine, and continental. The marine/continental type is for cases in which trajectories were over both land and water but were primarily over water. The continental/marine type was for cases in which trajectories were more over land. This is an additional categorization to the scheme used by Goodman. Analysis shows that our of 53 cases there were 17 marine cases, 25 marine/continental case, 7 continental/marine cases and 4 continental cases. An example of each trajectory type is given in figures Figure 10-Figure 13. The results of the trajectory analysis showed that it is difficult to discern differences, in terms of visibilities within clouds, between low cloud events having differing backwards trajectories. Visibility data for three cases of each of the four trajectory types showed very similar distributions. This was true even when data was analyzed on an event-by-event basis, as well as when data was normalized for ceiling height. 7. CONCLUSION FSO offers a unique solution to the last mile bottleneck. For the deployment of a successful network, it is essential to accurately determi ne availabilities. Use of airport data near the location of interest is often helpful, but may not provide a complete representation of the microclimate. Moreover, the visibility within a cloud is not yet very well studied, thus assumptions need to be made to evaluate the elevated link availability. The ceilometer and the three visibility sensors that Terabeam placed in downtown San Francisco allowed for a study of the visibility within low clouds. Wind data, satellite data, and multiple publications 3,5 determined that San Francisco s summertime low clouds are caused by sea fog. This is also evident in our from trajectory study since 42 for the 53 cases were marine or marine/continental cases. The ceilometer measured ceilings less than 300 meters nearly 27% of the time and ceilings less than 200 meters over 17% of the time. As one would expect, Site 3 reported the most minutes of low visibility, followed by Sites 2 and 1. It was made evident in our data that the visibility decreases with height into sea fog. This study provided an ideal example of a city where the airport surface availability deviated substantially from that near the city-center surface. Figure 5-Figure 9 show that the difference in number of hours of low visibility between the city core and airport may be on the order of few hours each month. The difference between the visibility at the airport and the elevated visibility recorded at Site 2 and Site 3 are quite pronounced. This indicates that correct availability estimation of high links cannot be calculated based on surface airport availability alone. In general cloud ceiling height data are expected to have a larger spatial correlation radius than surface visibility records, and this is reflected in our data. Although we saw much more in the way of low clouds downtown than were observed at the airport, the attenuation in these clouds was much less than ground based fog. Visibility in clouds dropped below 150 meters about 7% of the time, and almost never dropped below 150 m. These facts may allow us to increase availabilities for shorter links (less than about 500 meters) while decreasing projected availability for longer links at most altitudes. Further research is needed to more accurately determine visibility within low clouds. Though the information we have acquired concerning the visibility within sea fog has been immensely helpful, it will not be applicable to all cities. This study will be applicable to other cities that are affected by sea fog and will provide us with the necessary data to create improved elevated link availability. Studying visibility measurements within other types of low clouds and fog will bring further improvements to link budgets and availabilities and make free space optics more successful.

10 REFERENCES 1- J. A. Baars, M. Witiw, A. Al-Habash, and J. Ramaprasad, Determining Fog Type in the Los Angeles Basin Using Historic Surface Observation Data, 16 th Conference on Probability and Statistics in the Atmospheric Sciences, 13-17, AMS, Orlando, FL, R. J. Pilie, E.J. Mack, C.W. Rogers, U. Katz, and W.C. Kocmond, The formation of marine fog and the development of fog stratus systems along the California coast, J. Appl. Meteor. 18, , J. Goodman, J, The microstructure of California coastal fog and stratus, J. Appl. Meteo. 16, , HYSPLIT4 (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model, Web address: NOAA Air Resources Laboratory, Silver Spring, MD, J. Null, Climate of San Francisco Descriptive Narrative. Golden Gate Weather Services, 2001

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