WEATHER MODELING FOR ASTRONOMY OBSERVATIONS AT KILOHOKU OBSERVATORY, PEARL CITY, HAWAI`I

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1 WEATHER MODELING FOR ASTRONOMY OBSERVATIONS AT KILOHOKU OBSERVATORY, PEARL CITY, HAWAI`I Jonathan Bauer College of Arts and Science University of Hawaii at Hilo Hilo, Hawaii ABSTRACT Daily and monthly weather data were collected for a twelve month period to create a weather model profile for the astronomy observations at an observatory located near sea level. A database of daily and monthly weather conditions, to include factors such as humidity, temperature, wind direction, wind speed and rain totals was created to build the weather model. The weather measurements at the Kilohoku Observatory Weather Station (KOWS) were collected to analyze times during the study period when weather conditions would be optimal for astronomy observations as well as scheduling of maintenance, training and workshops and vacations. This weather model in conjunction with their plans to automate a telescope at the observatory will improve their research capabilities by harnessing valuable time resources, avoiding potential damage to equipment through warning of approaching poor weather and improving the quality of its astronomy sessions through its understanding of the surrounding weather phenomena. INTRODUCTION For the past twelve months weather measurements were collected and analyzed to establish an annual climate cycle for the Kilohoku Observatory. The purpose of the weather model is to understand the local climate and weather phenomena. By collecting daily and monthly periods of measurements specifically looking at factors of temperature, humidity, wind direction, wind speed and rain totals were used for the model. For comparison and interpretation, data from the NOAA metrological station at the Honolulu Airport PHNL (Station ID# ) was used to determine differences between the Kilohoku Observatory (KOWS) and NOAA PHNL station. The major reasoning for this is the topography gradient between the PHNL and KOWS stations are significant. Based on location measurements alone PHNL is approximately 17.4 km to the south by south east of KOWS. Including known knowledge of trade wind patterns in the Hawaiian Islands, from the north east, this means PHNL is in the down wind direction path of the city Honolulu, a major urban heating center on the island. Therefore it can be inferred that temperatures are influenced by this event to some extent. The location of the KOWS station however has an advantage in that it is located closer to the islands interior and not affected by this event and able to provide unbiased localized measurements. Study Area The location of KOWS on Oahu plays a major role in interpreting its climate. KOWS is located at the Kilohoku Observatory on the University of Hawaii s Leeward Community College

2 campus in Pearl City. Pearl City is in the south central region of the island were it is flanked on both sides by mountains, to the west the Waianae Range and to the east the Koolau Range in which the islands prominent northeast trade winds arrive from. The southern border of Pearl City is adjacent to Pearl Harbor that has three lochs, See Figure 1, the Leeward Community College and observatory are several meters from the middle lock. Figure 2 Shows Kilohoku Observatory located on the Leeward Community College campus in Pearl City and Pearl Harbor, See Figure 2. Figure 1. Topography of the Island of Oahu, Hawaii Figure 2. Location of the Kilohoku Observatory (white star), Leeward Community College campus, Pearl City, Hawaii METHODS The methods used involved collection of weather measurements from KOWS, creation of data files and analysis for a twelve month period. The weather station at KOWS was used to collect weather measurements for the Kilohoku Observatory allowing for specific monitoring of weather phenomena for the area of the observatory vicinity. Collection of the weather measurements data is preformed automatically by the sensors via radio transmission to the main unit WMR-968, See Figure3. The downside of the WMR-968 unit is that data is not stored longer than a 24 hour period. In events of power loss or other computer server issues the data will be lost. The WMR-968 unit, connected via RS-232 cable to a dedicated computer server, saves the real time measurements into Notepad files. Procedure and Data Analysis (Monitoring) The observatory s weather station and monitoring equipment includes a thermohygrometer, anemometer, baro-hygrometer, and rain gauge. The Oregon Scientific WMR-968 unit automatically saves the daily files to a dedicated computer server for weather monitoring. This computer server is setup for remote access to monitor the software, current real time observations and save data files while also allowing for system checks using another computer any where internet is available. Alerts are available through the weather station software program and are enabled to notify personnel of potential problems through and/or audible sounds.

3 Figure 3. Oregon Scientific WMR-968 weather station with a Thermo-Hygrometer, Anemometer, Baro-Hygrometer, and Rain Gauge. Figure 5. Location of the Anemometer, Thermo-Hygrometer, and Rain Gauge on the observatory s 7 meter tall radio antenna tower. Data collection and analysis is preformed in a two step process comprising of two substeps each. Step 1 is the (A) retrieval and (B) converting data files while Step 2 is the (A) creating formulas to convert from English to Metric system and (B) analysis by creating the proper charts and graphs to interpret and visualize the data. For Step 1A, retrieval of the weather data from KOWS is done by remote access via computer and the internet. This allowed for 24 hour access to the KOWS data computer server. Step 1B the automatically created files by the WMR-968 unit into Notepad were copied into Microsoft Excel spreadsheets for its abilities to create functions, produce charts and graphs for analysis. For Step 2A, formulas were created to convert English measurements into Metric measurements for temperature, rain totals and wind speed for scientific reporting. Other formulas were created to total daily and monthly measurements. In Step 2B, charts and graphs were created to plot data points on an hourly and daily scale. KOWS Measurements RESULTS KOWS climate data measurements were collected from 29 May 2009 to 8 May 2010 for a total of 305 days of weather measurements collected. This study examined the daily measurements of Temperature, Relative Humidity, Rain Total, Wind direction and Wind Speed to calculate daily and monthly mean values. Chart 1, Mean Monthly Temperatures vs. Relative Humidity, shows a possible inverse relationship between Relative Humidity (RH) and Temperature over the twelve month period. Chart 3, Mean Monthly Temperatures, details an expanded view of the mean monthly temperatures for the twelve month period and when compared with RH show another possible pattern. The line plot for Temperature from August to December 2009 seems to correlate with RH from October 2009 to February This could indicate that there is a lag time of two months between RH and Temperature.

4 The months June and September have received the lowest amounts of rainfall to date with 0.08mm and 0.06mm respectably, see Chart 2. The highest rainfall receiving month was found to be October with 1.91mm. Chart 2, Mean Monthly Rain Totals, also indicates the months October through December as the rainy season collecting between 1.91mm to 0.90mm of rain. It has been found that during this period the prominent wind direction for KOWS is form the east ranging between 100 to 141 cardinal degrees, see Chart 4. This contradicts the standard climate recognition of island winds originating from the Northeast. The following charts (1-5) display, temperature, relative humidity (RH), total rainfall and wind direction at Kilohoku Observatory Weather Station, KOWS. 1 1 Excluding days from June 13-17, 2009, October 10-15, 2009, March 25-April 20, 2010 and April 25-29, 2010 due to outages. 1 = June, 2 = July, 3 = August, 4 = September, 5 = October, 6 = November, 7 = December, 8 = January, 9 = February, 10 = March, 11 = April, 12 = May Chart 1. Mean Monthly Temperatures vs. Relative Humidity from June 2009 to May 2010 at the Kilohoku Observatory Weather Station, KOWS. Char 2. Mean Monthly Rain Totals from June 2009 to May 2010 at the Kilohoku Observatory Weather Station, KOWS. Chart 3. Mean Monthly Temperatures from June 2009 to May 2010 at the Kilohoku Observatory Weather Station, KOWS. Chart 4. Mean Monthly Wind Directions from June 2009 to May 2010 at the Kilohoku Observatory Weather Station, KOWS.

5 Chart 5. Mean Daily Temperatures from May 29, 2009 to May 8, 2010 at the Kilohoku Observatory Weather Station, KOWS Chart 6. Mean Daily Temperatures from May 29, 2009 to May 8, 2010 at the Kilohoku Observatory Weather Station, KOWS. Chart 7. Mean Daily Relative Humidity from May 29, 2009 to May 8, 2010 at the Kilohoku Observatory Weather Station, KOWS.

6 Discussion Optical cameras and spectrometer equipment used at the observatory require low amounts of relative humidity along with the least amount of water vapor in the atmosphere to operate with best results. Other sensitive equipment such as the electronics and telescopes, specifically the coated mirrors are highly sensitive to water vapor and salts that are suspended in the atmosphere as well. Optimal times for observation Based on the data collected thus far the period of mid June to July is the best continuous time span for astronomy observations. In addition the month of September showed promise as rain totals, temperature and relative humidity were all at low levels during the month. Worst times for observation It was found that the months from October through December were poor times for astronomy observations as high measurements of rain and relative humidity were collected. Recommendations This study in its creation of weather model is limited in that only one year of measurements were collected. It is recommended that this study/model include at least three to five years of data to correct for weather anomalies not normal to the region s climate profile. In addition to maintain the continuous data collection by the weather computer server it is recommended that a battery backup or more effective a solar powered battery power system be installed to constantly maintain the server and data collection. Turning off of the server s Microsoft updates would aid this as well. Few but several electrical outages and computer down time s were unforeseeable and disheartening as loses of data were great, with the most devastating March 25 through April 20, totaling 26 days of lost data. In all a total of 60 days were lost. CONCLUSION The collection of twelve months of weather measurements completed one year or annual cycle to produce a weather model for optimizing astronomy observations. The findings found that the optimal times of the year for observing, when humidity and temperature were low, were during the months of June and July. Optimal times for maintenance, upgrading of equipment and vacations was found to be during the months of October through December as these months received high amounts of rainfall and offered the least ability to perform astronomy observations. KOWS climate data results are for June 2009 to May To date 305 days of measurements have been collected, 29 May 2009 to 8 May 2010, with a loss of 60 days of data to equipment or other problems, June 13-17, 2009, October 10-15, 2009, March 25-April 20,

7 2010 and again on April 25-April 29. Factoring these lost days of data the research project had an 82.7 percent successful data collection rate. The twelve months of data can be used to benefit other research efforts by professionals, faculty and students for this area as no other NOAA COOP or other meteorological stations exist within the immediate area of Pearl City (NOAA, 2009). ACKNOWLEDGEMENTS A warm and deep thanks to Dr. Kakkala Mohanan, a great friend and mentor, for access to Kilohoku Observatory s weather station raw data along with the help of his student assistant researcher Rodolphe Herbrich who maintained the weather station including replacing malfunctioning sensors that were most valuable to the outcome of the research. A great thanks to Dr John Barnes Chief Scientist of NOAA s Mauna Loa Observatory for his mentorship and recommendations during my research. And last but not least, the NASA Hawaii Space Grant Consortium for funding this valuable research. REFERENCES He, Jiang, Akira Hoyano, and Takashi Asawa. "A numerical simulation tool for predicting the impact of outdoor thermal environment on building energy performance." Applied Energy 86 (2009): Hełminiak, Krzysztof G. "Impact of the atmospheric refraction on the precise astrometry with adaptive optics in infrared." New Astronomy 14 (2009): Meyn, Stephanie K., and T. R. Oke. "Heat fluxes through roofs and their relevance to estimates of urban heat storage." Energy and Buildings 41 (2009): NOAA. "NWS Cooperative Observer Program." National Oceanic and Atmospheric Administration. 4 June Suomalainen, Juha, Teemu Hakala, Eetu Puttonen, and Jouni Peltoniemi. "Polarised bidirectional reflectance factor measurements from vegetated land surfaces." Journal of Quantitative Spectroscopy & Radiative Transfer 110 (2009):