SO, HOW MUCH OF THE EARTH'S SURFACE IS COVERED BY RAIN GAUGES? The total area measured globally by all currently available rain gauges is surprisingly small, equivalent to less than half a football field or soccer pitch.
Precipitation, including both rainfall and snowfall, is a key component of the energy and water cycle influencing Earth s climate system. Its measurement is not only fundamental in specifying the current state of the distribution and intensity of precipitation that help define our climate, but also for monitoring the changes in our climate. Precipitation is considered to be an essential global variable (NASA 1988) and an essential climate variable (GCOS 2010) and, thus, requires adequate measurement. Fundamental to this must be high-quality, long-term observations at fine temporal and spatial resolutions. Trenberth et al. (2003) emphasized the need to be able to assess and quantify the changing character of precipitation through better documentation and processing of all aspects of precipitation. In particular, Stephens et al. (2010) noted that precipitation is not well represented in climate-scale models. Precipitation is also of great interest to a number of different scientific disciplines beyond the atmospheric community, including the hydrological, oceanic, cryospheric, environmental, ecological, and biological communities. Not only is precipitation a critical component of the Earth system, but also essential to life on Earth, impacting not only humanity, but also the natural environment around us. Over land, precipitation is ultimately the source of all freshwater. The monitoring and measurement of precipitation is of economic value for agriculture through agrobusinesses such as crop forecasting, water resource management, civil defense through mitigation of droughts or floods, and through more benign economic returns through, for example, the removal of particulate matter from the atmosphere (Thornes et al. 2010).
The measurement of precipitation (defined as deposition of water from the atmosphere in solid or liquid form) might at first appear to be straightforward; however, precipitation is relatively rare, highly variable, and consequently poorly monitored as an environmental parameter particularly on a global basis. Instantaneously, precipitation occurs globally probably less than 1% of the time (Barrett and Martin 1981). When precipitation does occur, intensities may range from very light to very heavy; the range of intensities for instantaneous precipitation is highly skewed toward lighter intensities. Furthermore, it has significant spatial and temporal variability, making it difficult to measure satisfactorily; dense observational networks are necessary to adequately capture this variability, particularly at fine temporal and spatial scales. Averaging over time and space generally results in accumulated precipitation being more normally distributed and more representative (Bell et al. 1990); climatological-scale accumulations require less dense networks, although these may not necessarily faithfully capture small-scale, extreme events or the variability over complex terrain.
Thus, the adequate measurement of precipitation is necessary at a number of scales and for a number of users. For flash flood studies precipitation measurements are required at local, fine scales with rapid access to the data (low latency) while for drought, longer-term measurements will suffice, with less stringent spatial, temporal, and latency requirements. For climate studies the accuracy of the measurements and the homogeneity of the data record are perhaps paramount over other criteria to enable the assessment of the subtleties due to climate change.
GAUGE NUMBERS. The number of gauges (see sidebar) cited in the literature varies somewhat. In their Catalogue of National Standard Precipitation Gauges, Sevruk and Klemm (1989b) put the number of gauges worldwide at more than 150,000, while Groisman and Legates (1995) estimated the number of "different" gauges to be as many as 250,000. However, New et al. (2001) put the number closer to the figure of 150,000 stations of Sevruk and Klemm. The figure was quantified by Strangeways (2003), who identified at least 123,014 monthly accumulation gauges (summarized in Table 1). These variations are largely dependent upon on the criteria used to count the number of gauges; for example, some of these numbers will include all the "stations" that have existed and have provided some precipitation measurements at some time in their observational record, while others will only report locations that currently return precipitation measurements. Thus, while it is certain that many gauges exist, not all gauges have operated continuously or simultaneously.
Not all gauge observations are available to the public or even to researchers. Those observations that are available are not necessarily available for all temporal samples (i.e., 3 hourly, daily, etc.) or with adequate data latency; flood monitoring and forecasting requires the timely delivery of data to be truly useful, whereas climate application can accommodate longer data delivery times. The availability of data from different countries/regions often depends upon the organization within the country, region, or locality. Often more than one agency within each country is tasked with the collection of rainfall data; these agencies are not necessarily consistent from one country to the next. An additional and potentially large number of gauge observations are available from commercial networks (e.g., water companies) although such data may be deemed to be commercially sensitive and therefore access to such data is often restricted.
Global meteorological data (including precipitation) are available through the World Meteorological Organization (WMO) Global Telecommunication System (GTS), collected from between 8,000 and 12,000 "first class" stations (WMO 2011). The precipitation information contained with the surface synoptic observation (SYNOP) report is collected for 3-hourly and daily periods at the fixed synoptic hours and distributed in near-real time, although the records for each station may not always be complete for an entire monthly record. Figure 1 illustrates the coverage of these measurements by mapping the distance from each of the GTS stations across the globe; it can be seen that the data coverage for near-real-time data on a global scale is relatively poor. While some regions such as Europe and eastern Asia (including Japan) have reasonable coverage, elsewhere gauges are sparse. This means that applications such as flash flood monitoring that require fine temporal and spatial resolutions generally rely upon gauge and radar (where available) observations obtained from local or regional meteorological organizations or satellite-based infrared estimates (Arkin and Xie 1994).
At the daily scale, the situation is somewhat better. A more comprehensive set of daily gauge data are organized through the Global Precipitation Climatology Project (GPCP) at the Global Precipitation Climatology Centre (GPCC; Becker et al. 2013), which provides perhaps the foremost repository of global precipitation data derived from gauges. Access to existing datasets hitherto unavailable to the GPCC has been improved through the WMO-implemented Global Terrestrial Network for Hydrology (GTN-H) observing system since 2001. Although the dataset released by the GPCC is restricted to a gridded product, it reveals the number of rain gauges operating across the globe that report information on a regular and reliable basis. As of 2013 (2015), a total of 180 institutions contribute data to the GPCC from about 85,000 (100,000) gauge locations that have provided observations at least once since the start of the dataset in 1901. Initial daily and monthly products are available a few days after the end of the integration period, with a more complete "monitoring" product after about 8 weeks and full daily and monthly products available after about two years. For this full, long-term, or climatological analysis it is critical to ensure continuous records of precipitation from any single station; consequently, the GPCC imposes a 10-yr minimum constraint. This restricts the number of available stations as of 2013 (2015) to 67,298 (75,165) for the best month, or 67,149 (75,033) for the worst, or a total of 65,335 (73,586) stations across all 12 months of the year (Becker et al. 2013; Schneider et al. 2015). Figure 2 shows the coverage of the GPCC gauge data. Most of Germany lies within 10 km of the nearest rain gauge, while large areas of Europe, the United States, eastern South America, India, and the more populated regions of Australia are less than 25 km from a gauge. Other regions with lesser, but still good, coverage include Turkey and Iran, parts of Africa (South Africa in particular), and the Andes in South America. Some of the GTS stations "disappear" in the GPCC dataset primarily because of the fragmented nature of their observational record.
A number of other key gauge data products exist that provide a greater range of precipitation products at varying temporal and spatial resolutions. It should be noted that many of these data products utilize the same gauge information as the GPCC product, rather than providing information from additional gauges. Such global datasets include the Climate Prediction Center (CPC) Gauge-Based Analysis of Global Daily Precipitation (Xie et al. 2010) and the Global Historical Climatology Network (GHCN; Menne et al. 2012), both of which provide daily gridded precipitation products derived from meteorological observations worldwide. The number of available gauges varies considerably by year (and by region/year) with a maximum (for precipitation observations) of just over 30,000 stations, about half of which are in the United States. The GHCN also collects information on snow depth from about 17,000 stations, again virtually all in the United States. The Climate Research Unit at the University of East Anglia gauge product (Mitchell and Jones 2005) aims to provide a consistent precipitation dataset exploiting historical precipitation records. Regional datasets, such as the Asian Precipitation--Highly Resolved Observational Data Integration Toward Evaluation of Water Resources (APHRODITE) product (Yatagai et al. 2012) and the China Gauge-based Daily Precipitation Analysis (CGDPA; Shen and Xiong 2016) are often able to obtain a greater number of regional gauges through local sources.
It is therefore clear that the number of gauges used in creating precipitation products varies considerably. The number of subdaily rainfall gauge observations available in near-real time is small, although more observations are available if the user is willing to wait longer for the data to become available. Daily gauge accumulations, although hindered by nonuniform reporting times globally, represent perhaps the greatest number of official data entries since this is in line with the WMO recommendations and most easily implemented by the individual meteorological agencies. At longer time scales the potential number of stations declines slowly, not least if a complete data record is required since some stations might not report precipitation (including zero rain) 100% of the time.
GAUGE REPRESENTATIVENESS. If the rain gauges alone are considered, the surface area of the orifices is surprisingly small. The most common gauges, as noted in Table 1, provide a total surface area estimated to cover just 3,026 [m.sup.2] from 123,014 gauges. Scaling the GTS and GPCC datasets using an average orifice size of 246 [cm.sup.2] would result in equivalent surface areas of about 295 and 1,612 [m.sup.2], respectively. For comparison, Table 2 provides the areas of pitches/courts/fields for common sporting activities; the comparisons between the GTS and GPCC against the equivalent areas are illustrated in Fig. 3. For the 3-hourly GTS dataset, assuming that the maximum number of gauges report data, an area just greater than that of the center circle of a soccer pitch is actually measured; in reality less than half of the GTS stations regularly report rainfall measurements. The GPCC gauges provide an area equivalent to about four basketball courts.
However, fundamental to the measurement of precipitation using rain gauges is that they are accurate at the location and are representative of their surrounding area. The "capture" of precipitation, particularly solid precipitation, by a rain gauge is largely affected by the wind effect around the orifice, an effect that is exacerbated with increased exposure (Duchon and Essenberg 2001; Goodison et al. 1998), together with losses or errors that may also arise from the mechanical construction of the gauge. However, despite errors associated with rain gauges, they remain arguably the most accurate instrument by which to measure rainfall. The measurement of snowfall is more difficult than the measurement of rainfall owing to the nature of falling (and blowing) snow, the variety of snow gauges used, and the catchment (inefficiencies of the gauges and is the focus of the WMO Solid Precipitation Intercomparison Experiment (SPICE) project (Nitu and Wong 2010b; Rasmussen et al. 2012). The majority of these measurements are now made by automated systems (Nitu and Wong 2010a), predominantly by weighing or tipping-bucket gauges, the latter being poor at measuring snowfall (Goodison et al. 1998). Despite the measurement accuracy for snowfall being strongly affected by the wind as a result of the collector-snow particle flow dynamics, only about 28% of precipitation gauges are equipped with shields to modify the airflow over the gauge, although most automated snow gauges are heated in order to prevent snow accumulating on the rim or sides of the collector (Nitu and Wong 2010a). While rainfall can be usually be measured to within 10%-20% (Vuerich et al. 2009), wind effects may result in less than 25% of the snowfall being caught (Goodison et al. 1998). However, errors and uncertainties associated with such precipitation measurements for manual gauges are reasonably well understood and corrections (or quality control) can be applied. The SPICE project is currently addressing corrections necessary for automatic gauges.
Spatially, at the very local scale, the gauge should at least represent the rainfall falling in its immediate vicinity, over scales of a few meters and preferably a few kilometers. However, gauge measurements have their limitations given the spatial and temporal variability of precipitation and the fact that gauges are (small) point measurements. Standards set by the WMO (2008) are designed to ensure consistency between gauge measurements to reduce some of the inherent errors, such as those caused by siting or exposure. However, even under ideal situations the representativeness or autocorrelation length of precipitation is surprisingly small; Habib et al. (2001) showed that for instantaneous precipitation over the midwestern United States the correlation coefficient between adjacent gauges fell to less than 0.5 just 4 km away; similar results were found for frozen precipitation. Furthermore, this correlation length is dependent upon the meteorology of the precipitation event and the local topography. Fortunately, accumulating precipitation over time increases the correlation length (Bell et al. 1990); over longer periods, the gauges become more representative of the regional precipitation regime. Although many schemes exist for the interpolation of precipitation, care is needed since the same interpolation scheme applied to instantaneous or monthly precipitation data could produce undesired results: indeed, the interpolation of instantaneous gauge data should be avoided where possible owing to the inherent heterogeneity of precipitation at fine temporal and spatial scales.
Considering the representativeness of gauges on a global scale, Fig. 4 illustrates the area of Earth within the defined distances from the GTS and GPCC gauge locations, divided into four regions: ocean or land and 60[degrees] poleward or 60[degrees]S-60[degrees]N. It is clear that the vast majority of Earth's surface closest to gauges is (not surprisingly) concentrated over the land areas between 60[degrees]S and 60[degrees]N, with relatively few gauges over land poleward of 60[degrees]. Over the oceans only a very small area is within 100 km of a gauge, and most of this area would be deemed "coastal waters." Considering the GPCC data globally, only 1.6% of Earth's surface lies within 10 km of a rain gauge, although 5.9% lies within 25 km; over 60[degrees]S-60[degrees]N land areas, this improves to 6.5% and 23.0%, respectively. This contrasts with less than 4% of Earth's oceans lying within 100 km of a gauge.
FILLING THE GAPS. It is clear that gaps exist within the currently available gauge networks over the various temporal scales, which require additional information if the representativeness of the precipitation measurements is sufficiently adequate to meet user requirements. Despite significant progress having been made in addressing some of the larger data gaps resulting from nonavailability of regional gauge datasets, it is also clear that not all existing rain gauges that could be used are currently exploited. The gauges incorporated into the GPCC database derive from meteorological agencies, which adhere to the requirements laid down by the WMO to ensure consistent measurements between different sites and regions. Perhaps the next great challenge will be whether, and how, to incorporate observations and/or measurements from nontraditional sources.
Citizen science or crowdsourcing offers one such source of additional information generated through addressing an underlying curiosity and interest in the weather [see Muller et al. (2015)]. An increasing number of Internet-enabled, low-cost sensors and instrumentation are now readily available for personal, research, and operational use. A number of these devices are capable of measuring precipitation--for example, tipping-bucket gauges or rainfall disdrometers [see Minda and Tsuda (2012)] connected to small computers (Goodwin 2013). The data collected (manually or electronically) by these devices can be transmitted via a range of communication techniques, making a large amount of data available in near-real time. Numerous websites have been set up to crowdsource data from these devices; these include the Community Collaborative Rain, Hail and Snow Network (CoCoRaHS: www.cocorahs.org; Cifelli et al. 2005), Weather Underground (www.wunderground .com), the Met Office Weather Observation Website (WOW: http://wow.metoffice.gov.uk; Tweddle et al. 2012), the NOAA Citizen Weather Observer Program (CWOP: http://wxqa.com), and gauge-enabled Netatmo weather stations (www.netatmo.com). Social media holds potential for providing information on the phase of precipitation. The National Oceanic and Atmospheric Administration's (NOAA) Precipitation Identification Near the Ground (PING) project (Binau 2012) and the mobile PING (mPING; Elmore et al. 2014) project provide information on the phase of precipitation to directly improve radar estimates of precipitation, while the "UK Snow Map" (http://uksnowmap.eom/#) was set up to monitor and map snowfall across the United Kingdom with citizens giving the snowfall a rating out of 10, in conjunction with a range of specific hashtags (e.g., #UKSnowMap, #UKSnow), while Muller (2013) used social media to obtain higher-resolution snow depths across Birmingham, United Kingdom.
The potential of harvesting amateur weather data from thousands of sites, which may now outnumber those of standard measurement sites, does have drawbacks however. Although the crowdsourced data have the potential to overcome the spatial and temporal representativeness of standard datasets, issues arise from utilizing nontraditional sources of data--that is, calibration, exposure, and other quality assurance/ quality control (QA/QC) issues (Muller et al. 2015). For example, Bell et al. (2015) found variations in annual rainfall totals from low-cost weather stations ranged from about 76% to 111% of standard collocated gauges, although after correction differences throughout the year rarely exceeded 5%. Another issue is that the locations of crowdsourced observations are population centric [see Elmore et al. (2014)]; while these additional data observations are not necessarily useful at the global scale, the fine temporal observations and the fact that they are population centric makes them ideal for certain applications, such as urban flood monitoring, since precipitation can vary significantly over short distances.
Radar networks, although not sources of direct measurements, provide another important source of large-scale rainfall information. Weather radars offer the advantage of providing frequent spatial observations of precipitation over relatively large areas compared to gauge observations. This spatial information provides additional insights into the variability of precipitation, particularly in the gaps between gauge observations. Although radars are capable of producing reasonable estimates of rainfall, they do suffer from a number of artifacts, not least persistent errors related to beam blockage and range effects, as well as transient errors resulting from imperfect backscatter to rainfall relationships. The spatial distribution of operational radars is also somewhat limited on a global scale, being limited primarily to the United States/Canada, Europe/western Russia, and Japan/ Korea/Australia and New Zealand; these are regions where the density of gauge data is generally adequate. Despite the drawbacks and some repetition of gauge coverage, radars can provide spatial measurements at time scales that fulfill a niche in the measurement of precipitation, at least on a local to regional scale.
Satellite observations of remotely sensed precipitation have been available over much of the globe for almost four decades and have the potential to be available on a truly global scale (Arkin and Ardunay 1994). In particular, satellite estimates have a distinct advantage for assessing precipitation over data-sparse regions such as the world's oceans. Satellite observations from visible, infrared, and, in particular, passive and active microwave systems are used to generate precipitation estimates using a number of techniques [see Kidd and Huffman (2011)], although techniques differ in performance regionally and temporally. The Tropical Rainfall Measuring Mission (TRMM; Kummerow et al. 1998) Precipitation Radar (PR) and the Global Precipitation Measurement (GPM) mission (Hou et al. 2014) Dual-frequency Precipitation Radar (DPR) provide more direct measurements of precipitation. Although the PR and DPR provide intermittent measurements covering 36[degrees]S-36[degrees]N and 66[degrees]S-66[degrees]N, respectively, the detailed information they provide is proving invaluable for a number of applications including hurricane monitoring and forecasting, as well as acting as a calibrator for other satellite precipitation measurements.
The potential for repurposing data from nonmeteorological networks has also shown potential. Numerous municipal networks exist and collect routine data for various applications and may have the potential to be used as proxies for monitoring variables such as precipitation. For example, Overeem et al. (2013) used the received signal-level data from microwave links in cellular communication networks to monitor precipitation in the Netherlands. Furthermore, multiobservational precipitation products have been developed to exploit the information from individual data sources. In particular, a number of mature satellite-based precipitation techniques incorporate surface precipitation datasets, allowing good spatial- and temporal-resolution precipitation products to be generated with the accuracy of surface measurements (e.g., Huffman et al. 2009): surface gauge measurements provide the anchor points for remotely sensed products.
CONCLUSIONS. The surface area that is equivalent to the orifice area for all of the worldwide operational rain gauges is surprisingly small, amounting to only 0.000000000593% of Earth's surface. There is clearly a large number of gauges in existence, but the actual number of gauges available to the user is highly variable depending upon the period of study and latency requirements. The GPCC rain gauge dataset, arguably the most comprehensive currently available global gauge dataset, comprises a little over 65,000 gauges whose combined area is roughly equivalent to less than half a soccer pitch. If the number of gauges that provide near-real-time data are considered, as available through the WMO GTS network, the gauges could easily fit into a tennis court or the center circle of a soccer pitch. However, since gauges represent more than just the actual point location of the orifice, it may be assumed that a greater part of Earth's surface might be covered: if each GPCC gauge represented an area extended to 5 km from each gauge (assuming no overlap) this still only represents about 1% of Earth's surface.
Improving worldwide information on precipitation is fundamentally important. Information utilizing crowdsourced precipitation measurements (as opposed to just observations) from "amateur" gauge networks has potential for many applications, including meteorology, but is probably more difficult to achieve because of timely access to the data, continuity, and absolute calibration of the measurements. Furthermore, the spatial availability of both amateur and crowdsourced information tends to mimic that of existing precipitation information as a result of being population centric. Great efforts have been made in obtaining gauge data in data-sparse regions; however, additional high-quality measurements are still needed to fill gaps in certain regions. In particular, the continental interiors of South America, Africa, and Australia together with the northern regions of the continental landmasses in the Northern Hemisphere and Antarctica are deficient in precipitation gauges. Projects such as the Trans-African HydroMeteorological Observatory (TAHMO; http://tahmo.org) are now beginning to address this need.
Ultimately, gauge data have a critical role to play in not only the observation and monitoring of Earth's climate, but also for enabling and improving other means of estimating global precipitation, whether through numerical models or through satellite observations.
ACKNOWLEDGMENTS. The authors thank the many colleagues who provided information and advice in the preparation of this paper. In particular the authors, along with other scientists, thank the many national meteorological agencies for their continued provision of gauge data to regional and global datasets; their data are invaluable in furthering precipitation science.
Arkin, P., and P. Ardunay, 1989: Estimating climatic-scale precipitation from space: A review. J. Climate, 2, 1229-1238, doi:10.1175/1520-0442(1989)002<1229:EC SPFS>2.0.CO;2.
--, and P. Xie, 1994: The Global Precipitation Climatology Project: First Algorithm Intercomparison Project. Bull. Amer. Meteor. Soc., 75, 401-419, doi:10.1175/1520-0477(1994)075<0401:TGPCPF>2 .O.CO;2.
Barrett, E. C., and D. W. Martin, 1981: The Use of Satellite Data in Rainfall Monitoring. Academic Press, 340 pp.
Becker, A., P. Finger, A. Meyer-Christoffer, B. Rudolf, K. Schamm, U. Schneider, and M. Ziese, 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901-present. Earth Syst. Sci. Data, 5, 71-99, doi:10.5194/essd-5-71-2013.
Bell, S., D. Cornford, and L. Bastin, 2015: How good are citizen weather stations? Addressing a biased opinion. Weather, 70, 75-84, doi:10.1002/wea.2316.
Bell, T. L., A. Abdullah, R. L. Martin, and G. R. North, 1990: Sampling errors for satellite-derived tropical rainfall: Monte Carlo study using a space-time stochastic model. J. Geophys. Res., 95, 2195-2205, doi:10.1029/JD095iD03p02195.
Binau, S., 2012: The PING Project. Accessed 16 November 2016. [Available online at http://mping.nssl .noaa.gov.]
Cifelli, R., N. Doesken, P. Kennedy, L. D. Carey, S. A. Rutledge, C. Gimmestad, and T. Depue, 2005: The Community Collaborative Rain, Hail, and Snow Network: Informal education for scientists and citizens. Bull. Amer. Meteor. Soc., 86, 1069-1077, doi:10.1175 /BAMS-86-8-1069.
Duchon, C. E., and G. R. Essenberg, 2001: Comparative rainfall observations from pit and above ground rain gauges with and without wind shields. Water Resour. Res., 37, 3253-3263, doi:10.1029/2001WR000541.
Elmore, K. L., Z. L. Flamig, V. Lakshmanan, B. T. Kaney, V. Farmer, H. D. Reeves, and L. P. Rothfusz, 2014: mPING: Crowd-sourcing weather reports for research. Bull. Amer. Meteor. Soc., 95, 1335-1342, doi:10.1175/BAMS-D-13-00014.1.
GCOS, 2010: Implementation plan for the Global Observing System for Climate in Support of the UNFCCC. WMO Tech. Doc. WMO/TD-1523, 186 pp. [Available online at www.wmo.int/pages/prog /gcos/Publications/gcos-138.pdf.]
Goodison, B. E., P. Y. T. Louie, and D. Yang, 1998: WMO solid precipitation measurement intercomparison. Instruments and Observing Methods Rep. 67, WMO/ TD-872, 212 pp. [Available online at www.wmo.int /pages/prog/www/IMOP/publications/IOM-67-solid -precip/WMOtd872.pdf.]
Goodwin, S., 2013: Raspberry Pi. Smart Home Automation with Linux and Raspberry Pi, M. Lowman, Ed., Apress Publishers, 275-296, doi:10.1007/978-l-4302-5888-9_8.
Groisman, P. Ya., and D. R. Legates, 1995: Documenting and detecting long-term precipitation trends: Where are we and what should be done? Climatic Change, 31, 601-622, doi:10.1007/BF01095163.
Habib, E., W. F. Krajewski, and G. J. Ciach, 2001: Estimation of rainfall interstation correlation. J. Hydrometeor., 2,621-629, doi:10.1175/1525-7541(2001)002<0621:EO RIC>2.0.CO;2.
Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701-722, doi:10.1175/BAMS-D-13-00164.1.
Huffman, G. J., R. F. Adler, D. T. Bolvin, and G. Gu, 2009: Improving the global precipitation record: GPCP version 2.1. Geophys. Res. Lett., 36, L17808, doi:10.1029/2009GL040000.
Kidd, C., and G. Huffman, 2011: Global precipitation measurement. Meteor. Appi, 18, 334-353, doi:10.1002/met.284.
Kummerow, C., W. Barnes, T. Korzu, J. Shuie, and J. Simpson, 1998: The Tropical Rainfall Measuring Mission (TRMM) sensor package. ]. Atmos. Oceanic Technol., 15, 809-917, doi:10.1175/1520-0426 (1998)015<0809:TTRMMT>2.0.CO;2.
Menne, M. J., I. Durre, R. S. Vose, B. E. Gleason, and T. G. Houston, 2012: An overview of the Global Historical Climatology Network-Daily database. J. Atmos. Oceanic Technol., 29, 897-910, doi:10.1175 /JTECH-D-11-00103.1.
Minda, H., and N. Tsuda, 2012: Low-cost laser disdrometer with the capability of hydrometeor imaging. IEEJ Trans. Electr. Electron. Eng., 7, S132-S138, doi:10.1002/tee.21827.
Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database of monthly climate observations and associated high-resolution grids. Int. J. Climatol., 25,693-712, doi:10.1002/joc,1181.
Muller, C. L., 2013: Mapping snow depth across the West Midlands using social media-generated data. Weather, 68, 82, doi:10.1002/wea.2103.
--, L. Chapman, S. Johnston, C. Kidd, S. Illingworth,
G. Foody, A. Overeem, and R. R. Leigh, 2015: Crowdsourcing for climate and atmospheric sciences: Current status and future potential. Int. J. Climatol., 35, 3185-3203, doi:10.1002/joc.4210.
NASA, 1988: Earth System Science: A Closer View. NASA, 208 pp.
New, M., M. Todd, M. Hulme, and P. Jones, 2001: Precipitation measurements and trends in the twentieth century. Int. J. Climatol., 21,1889-1922, doi:10.1002 /joc.680.
Nitu, R., and K. Wong, 2010a: CIMO survey on national summaries of methods and instruments for solid precipitation measurement at automatic weather stations. WMO Instruments and Observing Methods Rep. 102, WMO/TD-1544, 57 pp. [Available online at www.wmo.int/pages/prog/www/IMOP/publications /IOM-102_SolidPrecip.pdf.]
--, and--, 2010b: Measurement or solid precipitation at automatic weather stations: Challenges and opportunities. TECO-2010-WMO Technical Conf. on Meteorological and Environmental Instruments and Methods of Observation, Helsinki, Finland, WMO. [Available online at www.wmo.int/pages/prog /www/IMOP/publications/IOM-104_TECO-2010/l _Keynote_2_Nitu_Canada.doc.]
Overeem, A., H. Leijnse, and R. Uijlenhoet, 2013: Country-wide rainfall maps from cellular communication networks. Proc. Natl. Acad. Sci. USA, 110,2741-2745, doi:10.1073/pnas. 1217961110.
Rasmussen, R., and Coauthors, 2012: How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Amer. Meteor. Soc., 93, 811-829, doi:10.1175/BAMS-D-l 1-00052.1.
Robinson, A. C., and J. C. Rodda, 1969: Rain, wind and the aerodynamic characteristics of raingauges. Meteor. Mag., 98, 113-120.
Schneider, U., M. Ziese, A. Becker, A. Meyer-Christoffer, and P. Finger, 2015: Global precipitation analysis products of the GPCC. Global Precipitation Climatology Centre, 14 pp. [Available online at ftp:// ftp-anon.dwd.de/pub/data/gpcc/PDF/GPCC_intro_products_2008.pdf.]
Sevruk, B., and S. Klemm, 1989a: Types of standard precipitation gauges. Proc. WMI/IAHS/ETH Workshop on Precipitation Measurement, St. Moritz, Switzerland, WMO Tech. Doc. 32, 589 pp.
--, and--, 1989b: Catalogue of national standard precipitation gauges. Instruments and Observing Methods Rep. 39, WMO/TD-313, 50 pp. [Available online at www.wmo.int/pages/prog/www/IMOP /publications/IOM-39.pdf.]
Shen, Y., and A. Y. Xiong, 2016: Validation and comparison of a new gauge-based precipitation analysis over mainland China. Int. J. Climatol., 36, 252-265, doi:10.1002/joc.4341.
Stephens, G. L., and Coauthors, 2010: Dreary state of precipitation in global models. J. Geophys. Res., 115, D24211, doi:10.1029/2010JD014532.
Strangeways, I. C., 2003: Measuring the Natural Environment. 2nd ed. Cambridge University Press, 534 pp.
--, 2004: Improving precipitation measurement. Int. J. Climatol., 24, 1443-1460, doi:10.1002/joc.l075.
--, 2010: A history of rain gauges. Weather, 65, 133-138, doi:10.1002/wea.548.
Thornes, J., and Coauthors, 2010: Communicating the value of atmospheric services. Meteor. Appl., 17, 243-250, doi:10.1002/met.200.
Trenberth, K. E., A. Dai, R. M. Rasmussen, and D. B. Parsons, 2003: The changing character of precipitation. Bull. Amer. Meteor. Soc., 84, 1205-1217, doi:10.1175 /BAMS-84-9-1205.
Tweddle, J. C., L. D. Robinson, M. J. O. Pocock, and H. E. Roy, 2012: Guide to citizen science: Developing, implementing and evaluating citizen science to study biodiversity and the environment in the UK. Natural History Museum and NERC Centre for Ecology & Hydrology for UK-EOF, 29 pp. [Available online at www.ukeof.org.uk/documents/guide-to-citizen -science/view.]
Vuerich, E., C. Monesi, L. G. Lanza, L. Stagi, and E. Lanzinger, 2009: WMO field intercomparison of rainfall intensity gauges. WMO Instruments and Observing Methods Rep. 99, WMO/TD-1504,290 pp. [Available online at www.wmo.int/pages/prog/www /IMOP/publications/IOM-99_FI-RI.pdf.]
World Meteorological Organization, 2008: Guide to meteorological instruments and methods of observation. 7th ed. World Meteorological Organization WMO-8, 681 pp. [Available online at www.wmo.int /pages/prog/gcos/documents/gruanmanuals/CIMO /CIMO_Guide-7th_Edition-2008.pdf.]
--, 2011: Observing stations and WMO catalogue of radiosondes. WMO Publ. 9, Vol. A, accessed 8 August 2015. [Available online at www.wmo.int/pages/prog /www/ois/volume-a/vola-home.htm.]
Xie, P., M. Chen, and W. Shi, 2010: CPC unified gauge-based analysis of global daily precipitation. 24th Conf. on Hydrology, Atlanta, GA, Amer. Meteor. Soc, 2.3A. [Available online at https://ams.confex.com /ams/90annual/techprogram/paper_163676.htm.]
Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi, and A. Kitoh, 2012: APHRODITE: Constructing a long-term daily gridded precipitation dataset for Asia based on a dense network of rain gauges. Bull. Amer. Meteor. Soc., 93,1401-1415, doi:10.1175/BAMS-D-11-00122.1.
AFFILIATIONS: Kidd--University of Maryland, College Park, College Park, and NASA Goddard Space Flight Center, Greenbelt, Maryland; Becker--Deutscher Wetterdienst, Offenbach am Main, Germany; Huffman, Skofronick-Jackson, and Kirschbaum--NASA Goddard Space Flight Center, Greenbelt, Maryland; Muller--Royal Meteorological Society, Reading, and School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom; Joe--Meteorological Research Division, Environment and Climate Change Canada, Toronto, Ontario, Canada
CORRESPONDING AUTHOR E-MAIL: Chris Kidd, firstname.lastname@example.org
The abstract for this article can be found in this issue, following the table of contents.
WHAT IS A RAIN GAUGE?
Fundamentally, a rain gauge may be described as any object that collects rain(water) that can be measured. The most common gauges have historically been "simple cans" that accumulate rainwater over a set period of time; evidence of such gauges may be traced back over 2,000 years ago [see Strangeways (2010)]. While the basic concept of the gauge is simple, the practical implementation necessary to meet user requirements has led to a great diversity of gauge types; Sevruk and Klemm (1989a) identified more than 50 different manual gauge types alone. These can be categorized into the physical design of the gauge, the mechanisms used to collect and quantize the rainfall, and the technology necessary to report the rainfall.
Design: The vast majority of gauges share one common feature: the orifice. This is usually circular with the rim and interior designed to ensure an accurate catch of the precipitation. The differences in the size of the orifice do not appear to critically affect the accuracy of the catch (Strangeways 2003), most official gauges having orifices typically between about 127 and 400 [cm.sup.2]: Fig. SB1. (left) shows a Casella tipping-bucket rain gauge with a 400-[cm.sup.2] orifice together with a Snowdon Mkll accumulation gauge with a 127-[cm.sup.2] orifice. However, the wind flow over the orifice affects the accuracy of the catch, often resulting in an under-measurement for light intensity precipitation and stronger winds (Strangeways 2004). A number of designs therefore make the gauges more aerodynamic to reduce this undercatch (Robinson and Rodda 1969). An example of the adaptation of a rain gauge for measuring snowfall is shown in Fig. SB1 (right), which shows an OTT-Hydromet Pluvio2 200 weighing gauge with a heated rim, an inner Tretykov shield, and an outer alter fence.
Mechanical: Despite the simplicity of the accumulation gauge, the variability of precipitation over short time scales cannot be adequately captured by such gauges. Numerous mechanisms have therefore been devised to enable the precipitation collected to be suitably quantized over time. These include mechanically recording gauges, such as the siphon gauge and weighing gauges, and electrically recording devices such as tipping-bucket gauges, electronic weighing gauges, capacitance gauges, and drop counting gauges [see Strangeways (2010)].
Technological: The cost of manual or mechanically recorded gauges together with the development of electrically recording gauges has led to the development of (quasi) automatic gauges that can measure, record, and report the rainfall in near-real time through the use of electronic dataloggers and communication systems (satellite or phone networks). The availability of gauge measurements in near-real time greatly enhances the usefulness of such measurements for meteorological and hydrological applications.
Caption: Fig. SB1. (left) Two Casella tipping-bucket rain gauges (green) and a Snowdon Mkll accumulation gauge (copper color) at the University of Birmingham (United Kingdom) Winterbourne II climate station, and (right) an OTT-Hydromet Pluvio2 200 weighing gauge with a heated rim, an inner Tretykov shield, and an outer alter fence during the GPM Cold-season Precipitation Experiment (GCPEx) in Canada.
Caption: Fig. 1. Map showing the distance to nearest GTS gauge, typical of 3-hourly/daily measurements available in near-real time; blank areas in the figure are beyond 100 km from the nearest gauge.
Caption: Fig. 2. Map showing the distance to nearest GPCC gauge, typical of all regular and reliable gauge measurements; blank areas in the figure are beyond 100 km from the nearest gauge.
Caption: Fig. 3. Equivalent areas of common sports pitches and courts compared with the total areas of orifices of all GTS and GPCC gauges.
Caption: Fig. 4. Areas of Earth within certain distances from the nearest precipitation gauge for (left) the GTS network and (right) the GPCC dataset. The whole square represents the whole of Earth's surface, while the subdivisions are for land and ocean and 60[degrees]-poleward and 60[degrees]S-60[degrees]N.
Table 1. Monthly manually read gauges by type [after Strangeways (2003)]. Country of origin No. Countries deployed Germany (Hellmann) 30,080 30 China 19,676 3 United Kingdom (Mk2/Snowdon) 17,856 29 Russia 13,620 7 United States 11,342 6 India 10,975 1 Australia 7,539 3 Brazil 6,950 1 France 4,876 23 Total 123,014 Country of origin Orifice area Total ([m.sup.2]) ([cm.sup.2]) Germany (Hellmann) 200 601.6 China 314 617.8 United Kingdom (Mk2/Snowdon) 127 226.7 Russia 200 272.4 United States 324 367.5 India 200 219.5 Australia 324 247.5 Brazil 400 278.0 France 400 195.0 Total 3,026 Table 2. Dimensions and areas of common sporting fields/pitches/ courts together with numbers of gauges with the equivalent area. Dimensions Area Soccer pitch 105 x 68 m 7140.0 [m.sup.2] Center circle of soccer pitch 9.15-m radius 263.0 [m.sup.2] American football 109.7 x 48.8 m 5353.4 [m.sup.2] Tennis court 23.78 x 10.97 m 260.9 [m.sup.2] Basketball (FIBA) 28.0 x 15.0 m 420.0 [m.sup.2] Equivalent gauges (a) Soccer pitch 178,500-562,204 Center circle of soccer pitch 6,575-20,709 American football 133,834-421,524 Tennis court 6,522-20,541 Basketball (FIBA) 10,500-33,071 (a) Range based upon 400- to 127-[cm.sup.2] orifice areas.