Comparison of temperature and humidity measurements from two separate weather stations at Camden Airport, Australia
Matthew L. Riley
A *
A
Abstract
In Australia, meteorological measurements from air quality monitoring networks are an overlooked source of data for urban climatology and meteorology research and operations. The reasons for this are not clear but may include uncertainty in the quality of the observations. Here I compare over 1 million 1-min near surface air temperature (n = 516,334) and relative humidity (n = 516,717) measurements from two distinct observational stations at Camden Airport in New South Wales (NSW), Australia – the Bureau of Meteorology automatic weather station (AWS) and the NSW Department of Climate Change, Energy, the Environment and Water’s air quality monitoring station (AQMS). Annual mean bias in the AQMS temperature measurements was −0.14°C. There were seasonal and diurnal variations in temperature bias, with monthly mean bias varying from −0.27 to +0.06°C and mean hourly bias varying between −0.39 and +0.11°C. Annual mean bias in AQMS humidity measurements was −0.37%, monthly mean bias varied from −2.21 to +1.44% and bias in mean hourly measurements varied between −2.64 and +2.66%. Temperature and humidity mean biases were both within the range of the combined instrument uncertainties. The seasonal and diurnal signal in the bias is likely due to differences in instrument siting and the different radiation shields (Stevenson and multi-plate). This analysis suggests that temperature and humidity measurements from the NSW AQMS are of high quality. The performance of the AQMS measurements matches the AWS measurements and, for most circumstances, the temperature and humidity measurements are comparable. Urban climatologists and meteorologists should consider data from air quality networks in their research and can use this data with confidence.
Keywords: air quality monitoring station, AQMS, automatic weather station, AWS, humidity measurement, intercomparison, temperature measurement, thermometer screen.
References
Acquaotta F, Fratianni S, Aguilar E, Fortin G (2019) Influence of instrumentation on long temperature time series. Climatic Change 156(3), 385-404.
| Crossref | Google Scholar |
Ayers G (2019) A comment on temperature measurement at automatic weather stations in Australia. Journal of Southern Hemisphere Earth Systems Science 69(1), 172-182.
| Crossref | Google Scholar |
Ayers G, Warne J (2020) Response time of temperature measurements at automatic weather stations in Australia. Journal of Southern Hemisphere Earth Systems Science 70(1), 160-165.
| Crossref | Google Scholar |
Båserud L, Lussana C, Nipen TN, Seierstad IA, Oram L, Aspelien T (2020) TITAN automatic spatial quality control of meteorological in situ observations. Advances in Science and Research 17, 153-163.
| Crossref | Google Scholar |
Brandsma T, Van der Meulen J (2008) Thermometer screen intercomparison in De Bilt (the Netherlands)—part II: description and modeling of mean temperature differences and extremes. International Journal of Climatology: A Journal of the Royal Meteorological Society 28(3), 389-400.
| Crossref | Google Scholar |
Bojkovski J, Drnovšek J, Groselj D, Knez J, Begeš G (2019) WMO interlaboratory comparison in Europe with loops linkage and data processing. Journal of Atmospheric and Oceanic Technology 36(2), 257-267.
| Crossref | Google Scholar |
Bureau of Meteorology (2013) Observation specification number 2013.1. Guidelines for the siting and exposure of meteorological instruments and observing facilities. (The Bureau) Available at http://www.bom.gov.au/climate/cdo/about/observation_specification_2013.pdf [Verified 20 March 2024]
Bureau of Meteorology (2023) Climate Data Sites: Site information, Site Summary: CAMDEN AIRPORT AWS. (The Bureau) Available at http://www.bom.gov.au/climate/averages/tables/cw_068192_Info.shtml [Verified 20 March 2024]
Bureau of Meteorology (2024) Climate statistics for Australian locations. Monthly climate statistics: all years of record. Summary statistics: CAMDEN AIRPORT AWS. (The Bureau) Available at http://www.bom.gov.au/climate/averages/tables/cw_068192.shtml [Verified 20 March 2024]
Burt S (2022) Measurements of natural airflow within a Stevenson screen and its influence on air temperature and humidity records. Geoscientific Instrumentation, Methods and Data Systems 11(2), 263-277.
| Crossref | Google Scholar |
Burt S, de Podesta M (2020) Response times of meteorological air temperature sensors. Quarterly Journal of the Royal Meteorological Society 146(731), 2789-2800.
| Crossref | Google Scholar |
de Rosnay P, Browne P, de Boisséson E, Fairbairn D, Hirahara Y, Ochi K, Schepers D, Weston P, Zuo H, Alonso‐Balmaseda M (2022) Coupled data assimilation at ECMWF: current status, challenges and future developments. Quarterly Journal of the Royal Meteorological Society 148(747), 2672-2702.
| Crossref | Google Scholar |
Dutton JA (2002) Opportunities and priorities in a new era for weather and climate services. Bulletin of the American Meteorological Society 83(9), 1303-1311.
| Crossref | Google Scholar |
Farrance I, Frenkel R (2012) Uncertainty of measurement: a review of the rules for calculating uncertainty components through functional relationships. The Clinical biochemist. Reviews 33(2), 49-75.
| Google Scholar | PubMed |
Garcia-Marti I, Overeem A, Noteboom JW, de Vos L, de Haij M, Whan K (2023) From proof-of-concept to proof-of-value: approaching third-party data to operational workflows of national meteorological services. International Journal of Climatology 43(1), 275-292.
| Crossref | Google Scholar |
Genthon C, Six D, Favier V, Lazzara M, Keller L (2011) Atmospheric temperature measurement biases on the Antarctic Plateau. Journal of Atmospheric and Oceanic Technology 28(12), 1598-1605.
| Crossref | Google Scholar |
Hamdi R, Kusaka H, Doan Q-V, Cai P, He H, Luo G, Kuang W, Caluwaerts S, Duchêne F, Van Schaeybroek B (2020) The state-of-the-art of urban climate change modeling and observations. Earth Systems and Environment 4, 631-646.
| Crossref | Google Scholar |
Harrison R, Wood C (2012) Ventilation effects on humidity measurements in thermometer screens. Quarterly Journal of the Royal Meteorological Society 138(665), 1114-1120.
| Crossref | Google Scholar |
Harrison RG, Burt SD (2021) Quantifying uncertainties in climate data: measurement limitations of naturally ventilated thermometer screens. Environmental Research Communications 3(6), 061005.
| Crossref | Google Scholar |
Hirsch AL, Evans JP, Thomas C, Conroy B, Hart MA, Lipson M, Ertler W (2021) Resolving the influence of local flows on urban heat amplification during heatwaves. Environmental Research Letters 16(6), 064066.
| Crossref | Google Scholar |
Hoover J, Yao L (2018) Aspirated and non‐aspirated automatic weather station Stevenson screen intercomparison. International Journal of Climatology 38(6), 2686-2700.
| Crossref | Google Scholar |
Khan HS, Santamouris M, Paolini R, Caccetta P, Kassomenos P (2021) Analyzing the local and climatic conditions affecting the urban overheating magnitude during the Heatwaves (HWs) in a coastal city: a case study of the greater Sydney region. Science of The Total Environment 755, 142515.
| Crossref | Google Scholar |
Koopmans S, van Haren R, Theeuwes N, Ronda R, Uijlenhoet R, Holtslag AA, Steeneveld GJ (2023) The set‐up and evaluation of fine‐scale data assimilation for the urban climate of Amsterdam. Quarterly Journal of the Royal Meteorological Society 149(750), 171-191.
| Crossref | Google Scholar |
Lacombe M, Bousri D, Leroy M, Mezred M (2011) WMO field intercomparison of thermometer screens/shields and humidity measuring instruments, Ghardaia, Algeria, November 2008–October 2009. World Meteorological Organization, Instruments and Observing Methods Report 106, 101.
| Google Scholar |
Lean P, Hólm E, Bonavita M, Bormann N, McNally A, Järvinen H (2021) Continuous data assimilation for global numerical weather prediction. Quarterly Journal of the Royal Meteorological Society 147(734), 273-288.
| Crossref | Google Scholar |
Leeper RD, Rennie J, Palecki MA (2015) Observational perspectives from US Climate Reference Network (USCRN) and Cooperative Observer Program (COOP) network: temperature and precipitation comparison. Journal of Atmospheric and Oceanic Technology 32(4), 703-721.
| Crossref | Google Scholar |
Li M, Shao Q, Dabrowski JJ, Rahman A, Powell A, Henderson B, Hussain Z, Steinle P (2024) Developing a statistical approach of evaluating daily maximum and minimum temperature observations from third-party automatic weather stations in Australia. Quarterly Journal of the Royal Meteorological Society 150(760), 1624-1642.
| Crossref | Google Scholar |
Lucas‐Picher P, Argüeso D, Brisson E, Tramblay Y, Berg P, Lemonsu A, Kotlarski S, Caillaud C (2021) Convection‐permitting modeling with regional climate models: latest developments and next steps. Wiley Interdisciplinary Reviews: Climate Change 12(6), e731.
| Crossref | Google Scholar |
Marsigli C, Ebert E, Ashrit R, Casati B, Chen J, Coelho CA, Dorninger M, Gilleland E, Haiden T, Landman S (2021) Observations for high-impact weather and their use in verification. Natural Hazards and Earth System Sciences 21(4), 1297-1312.
| Crossref | Google Scholar |
Matsuguchi M, Hirota E, Kuroiwa T, Obara S, Ogura T, Sakai Y (2000) Drift phenomenon of capacitive‐type relative humidity sensors in a hot and humid atmosphere. Journal of the Electrochemical Society 147(7), 2796.
| Crossref | Google Scholar |
Mills G (2014) Urban climatology: history, status and prospects. Urban Climate 10, 479-489.
| Crossref | Google Scholar |
Naserikia M, Hart MA, Nazarian N, Bechtel B, Lipson M, Nice KA (2023) Land surface and air temperature dynamics: the role of urban form and seasonality. Science of The Total Environment 905, 167306.
| Crossref | Google Scholar | PubMed |
Navarro-Serrano F, López-Moreno JI, Azorin-Molina C, Buisán S, Domínguez-Castro F, Sanmiguel-Vallelado A, Alonso-González E, Khorchani M (2019) Air temperature measurements using autonomous self-recording dataloggers in mountainous and snow covered areas. Atmospheric Research 224, 168-179.
| Crossref | Google Scholar |
Nipen TN, Seierstad IA, Lussana C, Kristiansen J, Hov Ø (2020) Adopting citizen observations in operational weather prediction. Bulletin of the American Meteorological Society 101(1), E43-E57.
| Crossref | Google Scholar |
Pérez IA, García MÁ, Sánchez ML, Pardo N, Fernández-Duque B (2020) Key points in air pollution meteorology. International Journal of Environmental Research and Public Health 17(22), 8349.
| Crossref | Google Scholar | PubMed |
Rennie S, Cooper S, Steinle P, Dietachmayer G, Krysta M, Franklin C, Bridge C, Marshall M, Xiao Y, Sgarbossa D (2022) ACCESS-C: Australian convective-scale NWP with hourly 4D-Var data assimilation. Weather and Forecasting 37(7), 1287-1303.
| Crossref | Google Scholar |
Richardson SJ, Brock FV, Semmer SR, Jirak C (1999) Minimizing errors associated with multiplate radiation shields. Journal of Atmospheric and Oceanic Technology 16(11), 1862-1872.
| Crossref | Google Scholar |
Santamouris M, Haddad S, Fiorito F, Osmond P, Ding L, Prasad D, Zhai X, Wang R (2017) Urban heat island and overheating characteristics in Sydney, Australia. An analysis of multiyear measurements. Sustainability 9(5), 712.
| Crossref | Google Scholar |
Shuman CA, Steffen K, Box JE, Stearns CR (2001) A Dozen Years of Temperature Observations at the Summit: central Greenland Automatic Weather Stations 1987–99. Journal of Applied Meteorology 40(4), 741-752.
| Crossref | Google Scholar |
Stalker J, Lasley J, Frederick G, McPherson R, Campbell P, Philips B, Pasken B (2013) A nationwide network of networks. Bulletin of the American Meteorological Society 94(10), 1602-1606.
| Crossref | Google Scholar |
Trewin B (2022) A climatology of short-period temperature variations at Australian observation sites. Journal of Southern Hemisphere Earth Systems Science 72(2), 117-125.
| Crossref | Google Scholar |
Trewin B, Braganza K, Fawcett R, Grainger S, Jovanovic B, Jones D, Martin D, Smalley R, Webb V (2020) An updated long-term homogenized daily temperature data set for Australia. Geoscience Data Journal 7(2), 149-169.
| Crossref | Google Scholar |
Ulpiani G, Duhirwe PN, Yun GY, Lipson MJ (2022) Meteorological influence on forecasting urban pollutants: long-term predictability versus extreme events in a spatially heterogeneous urban ecosystem. Science of The Total Environment 814, 152537.
| Crossref | Google Scholar | PubMed |
Ulpiani G, Ranzi G, Santamouris M (2021) Local synergies and antagonisms between meteorological factors and air pollution: a 15-year comprehensive study in the Sydney region. Science of The Total Environment 788, 147783.
| Crossref | Google Scholar | PubMed |
Wieringa J (1980) Representativeness of wind observations at airports. Bulletin of the American Meteorological Society 61(9), 962-971.
| Crossref | Google Scholar |
Zhang C, Fan C, Yao W, Hu X, Mostafavi A (2019) Social media for intelligent public information and warning in disasters: an interdisciplinary review. International Journal of Information Management 49, 190-207.
| Crossref | Google Scholar |
