Philippine Journal of Science 150 (1): 53-66, February 2021 ISSN 0031 - 7683 Date Received: 04 May 2020

ClimDatPh: An Online Platform for Philippine Climate Data Acquisition

Marcelino Q. Villafuerte II1*, John Carlo R. Lambrento1,2 Christian Mark S. Ison1, Abigail Allen S. Vicente1, Rosalina G. de Guzman1, and Edna L. Juanillo1

1Department of Science and Technology – Philippine Atmospheric, Geophysical and Astronomical Services Administration (DOST-PAGASA) 2Manila Observatory, City,

Climate-related information has a lot of potential applications; yet, they are often not easily accessible. This study addresses such an issue by providing a detailed description of climate data collection, quality control (QC) procedures, and steps in acquiring the datasets from an online platform called “ClimDatPh.” The platform allows easier access to the quality-controlled principal surface climate data provided by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA). In the first few months of existence on the internet (from September–December 2019), the online platform has served hundreds of climate data users from different parts of the Philippines and in many other countries across the globe. Preliminary statistics indicate that the climate datasets were requested for use in several applications, including scientific inquiries related to water and hydrology (21%), weather and climate (16%), energy (14%), agriculture and fisheries (11%), environment and biodiversity (10%), infrastructure and construction projects (6%), disaster risk reduction and management (5%), health and safety (5%), soil and land use (4%), and many other useful applications (8%). The use of climate data, primarily in agriculture, is further demonstrated in this study. Derived climate indices indicated that rainfall shortages caused by El Niño events have led to historical rice production losses in the Philippines. With the availability of internet access, it is expected that many more basic and applied research utilizing the Philippine climate data will be pursued in the future, which in turn would be helpful to increase scientific understanding of how the country’s climate behaves and affects other sectors.

Keywords: climate data, climatic impacts, El Niño, online platform, rice production, the Philippines

INTRODUCTION air quality indicators, and soil protection, among others – have been derived from using climate data (Biavetti et Climate data serve as the foundation of our understanding al. 2014). However, the growing interest of the research of climatic processes, variability, and extremes (Thorne et community to employ climate data is hampered by its lack al. 2017). Various ingenious applications – including crop of accessibility and/or detailed description of available growth prediction, genomic selection of certain crops, climate variables (Brunet et al. 2008; Biavetti et al. 2014; Longman 2018). Internet-based climate data platforms are *Corresponding Author: mvillafuerte@.dost.gov.ph proven solutions to address such problems. For instance, the

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Integrated Surface Database (Smith et al. 2011) and Climate in ClimDatPh. Preliminary statistics of four months’ worth Data Online (CDO) maintained by the National Centers (September–December 2019) of successfully served data for Environmental Information, formerly the National requests through the ClimDatPh platform is also presented Climatic Data Center or NCDC of the National Oceanic and to gauge its efficiency in delivering data requests. Finally, Atmospheric Administration of the United States (NOAA an example use case of ClimDatPh-retrieved data is 2019), as well as the Southeast Asian Climate Assessment provided to illustrate the potential applicability of climate & Dataset (SACA&D 2020; van den Besselaar et al. 2015) data in the agriculture sector. have increased climate data usage. Accessibility to climate data with a pertinent description from such online portals contributes highly to sectoral innovations. Some examples include the development of climatic design information METHODS on distribution and installation of heating and ventilating This section provides comprehensive information on how equipment (Smith et al. 2011), determination of the timing climate datasets were derived and made available to the of intervention for vector-borne diseases (Ceccato et al. public. It begins with the data collection, QC procedures, 2018), and identification of the impacts of El Niño events and subsequent archiving of climate data. A brief in the Amazon ecosystem (Li et al. 2011). description of the climate variables and the instruments In the Philippines, access to local climate data entails used to measure them, as well as the frequency and timing requesting parties to either personally visit or send of meteorological observations, are also provided. Finally, an electronic mail to PAGASA’s Climatology and the simple steps to follow in acquiring data through the Agrometeorology Division. Such a process is inefficient as ClimDatPh are presented. it may require the physical presence of requesting parties or unclearly defined subsequent actions prior to obtaining the Data Collection and QC Procedures climate data. Nevertheless, it has produced interdisciplinary Meteorological observations are regularly conducted sectoral studies such as the use of climate data in relation to at PAGASA stations during the main standard time of incidences of dengue (Carvajal et al. 2018), leptospirosis observations – as recommended by the WMO at 00:00, (Matsushita et al. 2017), influenza (Arguelles et al. 2019), 06:00, 12:00, and 18:00 Universal Time Coordinated heat waves (Seposo et al. 2017), and temperature effects on (UTC), as well as during the intermediate standard time human mortality (Seposo et al. 2015). Climate data from of observations taken at 03:00, 09:00, 15:00, and 21:00 PAGASA have also been used to investigate policy-relevant UTC. It has to be noted that the topics related to education (e.g. Villafuerte et al. 2017), the (PhST) is equivalent to UTC + 08:00. This means that the impacts of El Niño on yellow corn yield (Tongson et al. daily summaries, as discussed later, span two different 2017), and the forecast of amounts of solar photovoltaic days in the Philippines. The observed variables are written energy generation (Creayla et al. 2017). However, the by the weather observers on the PAGASA observation utilization of PAGASA climate data is still not maximized. forms and undergo a series of QC procedures prior to Global data sharing standards dictate that datasets should dissemination. Figure 1 illustrates the three levels of QC be findable, accessible, interoperable, and reusable to lead procedures routinely being done in PAGASA. to an array of products for various sectors (Popkin 2019). An initial QC procedure (QC0) is conducted at the station Such data must be free and unrestricted for non-commercial right after the observations were taken and prior to data activities (at the least) to serve the research and educational transmission. The procedures involved in QC0 include data communities in reference to the recommendations of completeness, consistency checking, and physical limit the World Meteorological Organization (WMO 2018a). tests. Consistency checking includes both internal (e.g. dry Anchored with the data policy provided by the Freedom bulb temperature ≤ maximum temperature) and temporal of Information Executive Order of the Republic of the (variations through time, i.e. sudden drop or increase in Philippines (E.O. No. 2 s. 2016) for policy development and temperature) consistencies. Summation values are also the implementation of Republic Act 10692 (R.A. No. 10692), tested for consistency (i.e. consistency of accumulated also known as the “PAGASA Modernization Act of 2015,” an values over 6 h with 3-hourly records). Physical limit internet-based platform called “ClimDatPh” is established. tests for climate variables are likewise performed based The online platform allows climate data users to submit their on physical constraints [e.g. wind direction (WD) value requests and for PAGASA to assess and validate the requests should not be greater than 360°, relative humidity (RH) made, and efficiently provide the data being requested. should not be greater than 100%]. Once the data have This paper aims to describe the available climate data, the passed QC0, the stations’ personnel send them in coded procedures done to ensure the quality of the data, and the form following the WMO standard format of the synoptic simple steps to follow for acquiring the digitized raw data message to the PAGASA Central Office (PAGASA-CO)

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Figure 1. Schematic diagram of data transmission and QC procedures undergone by the climate data prior to making available in ClimDatPh. Boxes highlighted in red show the main checkpoint for QC procedure. The acronyms used are defined in the text.

via a short message service, internet, or telephone. These System (MDRIMS) for daily operations of the WFS and coded messages are sent to the PAGASA-CO within 20 Farm Weather Services Section (FWSS) of PAGASA, and (3) min after the observations were taken. to the PAGASA Unified Meteorological Information System (PUMIS) for internal archiving and further QC tests. The The coded sub-daily climate data received at the PAGASA- entire procedures from QC0 to QC1 need to be done within CO undergo a near-real-time QC (QC1). Manual scrutiny 30 min to serve its operational purpose. on formatting, missing values, internal consistency, and physical limits similar to the techniques used in QC0 are Accomplished station-verified PAGASA observation conducted again in QC1 to identify errors that might have forms and recorded supplemental data (e.g. thermograph, been introduced in the data transmission. A computer hygrothermographs, rainfall charts, etc.) are forwarded program called Synoptic Decoder or Sycoder is used in QC1 from synoptic stations to the PAGASA-CO, where non- to probe summation errors. Spatial coherence testing is also real-time QC (QC2) is performed (the lowermost portion done in QC1 when suspiciously large or unusually small of Figure 1). Each of the stations is required to submit values are found at a particular station. Visual comparison their observation forms containing daily and sub-daily with neighboring stations, as well as manual checking of summaries together with supplemental information significant weather events (e.g. thunderstorm or tropical every 10th day of the month for observations taken in cyclone occurrence) over the area is done by the weather the previous month. However, it takes a month or even plotters assigned at the Weather Forecasting Section (WFS) longer for these forms to arrive at the PAGASA-CO, of PAGASA. If deemed erroneous, detected errors are relayed especially for remotely located stations. Once received at to the station’s weather observer. Commonly detected errors the PAGASA-CO, QC2 is performed. Similar procedures are typographic in nature, which mainly occur during the employed in QC0 and QC1 for data completeness, transmission of coded messages. Acknowledgment of an consistency checking, and physical limit tests are repeated identified error in QC1 by the weather observer assigned at with the sub-daily observations during the QC2, but the station is done by re-sending the corrected coded message available supplemental data from synoptic stations are and rectifying the PAGASA observation forms. The data that utilized to further scrutinize the accuracy of information. passed QC1 are then sent to three portals: (1) WMO Global In QC2, emphasis on the consistency of the data entered Telecommunication System for international data sharing, among different sections of the PAGASA observation (2) Meteorological Data Relay and Information Management forms is done. This is aimed to establish the accuracy of

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submitted PAGASA observation forms before data are older database (prior to PUMIS) have been transferred stored and archived in the database. Errors found during to the system, which are then extracted and stored the QC2 are flagged with a red pen on the PAGASA in the ClimDatPh’s database to serve the data users' observation forms. Commonly detected errors (e.g. community. The consistency of these extracted climate inconsistency between the daily summaries and sub-daily data is checked against the daily and monthly values observations) are due to the manual computations being from the QCed PAGASA observation forms stored in done by the observers, while some errors emanate during CADS. Discrepancies on daily data are highlighted and the climate data transcription. corrections are manually encoded. Once the encoding of sub-daily data from the QCed PAGASA observation forms in PUMIS is completed, the electronic daily data Data Archiving stored in ClimDatPh’s database is verified against the Data archiving and digitization follow after the entire QC PUMIS-derived data to correct possible input errors and procedures (from QC to QC ) are done. The hard copies 0 2 inconsistencies, as well as to update the records. This is of observation forms (QCed PAGASA observation forms necessary to minimize errors that might be introduced referred to in the lowermost panel of Figure 1) are stored during data management chain collection, processing, at the Climate and Agrometeorological Data Section transfer, and storage (Longman et al. 2018). Hence, (CADS) of PAGASA, where data entry (digitization) takes quality is assured for the climate data prior to archiving place. This entire process chain has been in practice for a and disseminating through the ClimDatPh. long period in PAGASA. It was only in 2014 that a new climate database management system called PUMIS was Seven climate variables are made available in ClimDatPh established. PUMIS allows data entry right at the stations (Table 1). The instruments used in measuring them as well where the actual meteorological observations were taken. as the methods for deriving the daily, monthly, and annual Also, the entire QC procedures (from QC0 to QC2) can be values are briefly described as follows: done digitally within PUMIS and automatic data flagging based on historical, variable-specific meteorological 1. Rainfall (RR) is the vertical depth of water that reaches extremes, and thresholds guide human analysts on possible the ground. A representative sample measurement erroneous data. Consistency among the different observation of RR is obtained from PAGASA stations using a forms is ensured in PUMIS because the daily, monthly, and standard 8-in rain gauge. The RR for a particular day annual climate data are electronically generated by the is the accumulated amount within the 24-hr period system from the sub-daily values. However, as of writing (i.e. the sum of RR taken at 06:00, 12:00, and 18:00 this paper, the full functionalities of PUMIS are yet to be UTC of that day and the RR taken at 00:00 UTC realized and fully implemented in PAGASA. The entire of the following day). Translating this to local time process is also hindered by poor internet connectivity or the implies that the RR for a particular day corresponds absence of internet connection at some stations. to the accumulated amount from 08:01 PhST of that day to 08:00 PhST of the following day. The monthly As of this writing, historical sub-daily climate data RR is then taken as the sum of the daily RR covering covering the period from 2014–2018 have been entered a particular month, and the annual RR is taken as the in PUMIS, while earlier and more recent years are yet sum of the monthly RR summarized every year. to be encoded. Nevertheless, the daily data from the

Table 1. List of available ClimDatPh climate variables together with their corresponding units of measurement and sub-daily periods of observation where daily, monthly, and annual climate data are derived from. Climate variable Unit of measurement Sub-daily periods of observationa Rainfall (RR) mm Six-hourly (main standard time) Mean temperature (Tmean) °C Six-hourly (main standard time) Maximum temperature (Tmax) °C Six -hourly (main standard time) Minimum temperature (Tmin) °C Six-hourly (main standard time) Wind speed (WS) m s-1 Three-hourly (main and intermediate standard times)

Wind direction (WD) degree (°) relative to true north Three-hourly (main and intermediate standard times) Relative humidity (RH) % Three-hourly (main and intermediate standard times) aThe sub-daily period of observations is being conducted more frequently at some stations (e.g. stations situated at international airports), but the daily values are obtained uniformly from the main standard time of observations (i.e. 0000, 0006, 1200, and 1800 UTC). Data requests for sub-daily data are subject to data availability.

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2. Near-surface air temperature is measured at a and the annual WS (WD) is taken as the mean (mode) of representative height of 1.25–2.00 m above the ground. the monthly WS (WD) summarized every year. Temperature sensors (typically consisting of minimum, maximum, dry-bulb, and wet-bulb thermometers) 4. RH is the ratio of actual vapor pressure and saturation are securely attached inside a louvered screen or an vapor pressure of the air for the prevailing temperature instrument shelter, whose floor is at a height of 1.25 m at a height of 1.25–2.00 m above the ground. above the ground and the lower layer of the roof is 2.00 Calculations are attained from dry-bulb and wet- m above the ground. The instrument shelter protects bulb temperature readings, either through the use of the thermometers from radiant heat, precipitation, and psychrometric tables or calculators. The daily RH other external factors that may affect air temperature values for synoptic stations are the average of all the measurement (WMO 2018b). RH readings (taken at an equal interval, three-hourly, or six-hourly depending on the station) within the day. a. Maximum temperature (Tmax) is the highest The monthly RH values are then taken as the mean of thermometer reading obtained from the six-hourly the daily RH values covering a particular month, and time interval taken at the main standard time of the annual RH is taken as the mean of the monthly RH observations. The daily Tmax is the highest value summarized every year. from the six-hourly readings within the 24-hr period corresponding to 06:00, 12:00, and 18:00 The daily, monthly, and annual values of these climate UTC of a particular day and the observation taken variables have been made available through the at 00:00 UTC of the following day. The monthly ClimDatPh from 55 synoptic stations of PAGASA (Figure Tmax is then taken as the mean of the daily Tmax 2). Additional information containing available time values covering a particular month, and the annual periods, documented changes, and the stations’ geographic Tmax is taken as the mean of the monthly Tmax location are provided in Appendix Table I. summarized every year. b. Minimum temperature (Tmin) is the minimum thermometer reading at six -hourly intervals. The daily Tmin is the lowest value from the six-hourly readings obtained similar to the times when Tmax and RR are taken. The monthly Tmin is then taken as the mean of the daily Tmin values covering a particular month, and the annual Tmin is taken as the mean of the monthly Tmin summarized every year. c. Mean temperature (Tmean) is the sum of Tmax and Tmin divided by two for each six-hourly readings. Calculations for daily Tmean follows the same approach but with the use of daily Tmax and Tmin values. Specifically, the value of Tmean at day i in a particular location is Tmeani = (Tmaxi + Tmini)/2. The monthly Tmean is then taken as the mean of the daily Tmean values covering a particular month, and the annual Tmean is the mean of the monthly Tmean summarized every year. 3. Wind speed (WS) is the average speed of the wind over a 10-min period, while WD is the nearest 10-degree direction of the wind’s origin with reference to true north. An anemometer and a wind vane exposed at a height of 10 m above the ground are used to measure WS and WD, respectively. The WS observations taken at an equal Figure 2. Geographic map of the Philippines showing the location interval (e.g. six-hourly or three-hourly) during the day of PAGASA synoptic stations that are made available in ClimDatPh. Synoptic stations marked with white dots are averaged to get the daily readings while the most in the middle of the circles have sufficient records of commonly occurring WD (i.e. the mode) is recorded RR, Tmax, and Tmin (< 5% missing values during the as the daily prevailing WD for synoptic stations. The period 1987–2018). monthly WS (WD) values are taken as the mean (mode) of the daily WS (WD) values covering a particular month,

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Data Requests and Acquisition information and validation requirements submitted by the The complete procedures for requesting climate data user are carefully handled within CADS following the through the ClimDatPh involve four simple steps agency’s privacy policy. illustrated in Figure 3. Data requests are performed through the PAGASA website at the webpage http://bagong. Data requests sent through the ClimDatPh platform pagasa.dost.gov.ph/climate/climate-data. Weblinks are assessed and processed in CADS. Specific climate facilitated through Google Forms are made available variables and needed timescales (i.e. daily, monthly, and/ based on the user’s purpose. For efficient data requests, or annually) at a particular location are electronically data users should be aware of the climate variables generated as comma-separated value (CSV) files. A they need and the station nearest to their area of study script written in R statistical programming language is prior to registration. Once the needed stations and the used to extract the requested data from the ClimDatPh’s climate variables were identified, requesting individuals database. The processed data are then emailed to the need to input their personal information directly on the requesting party within at least three working days in online request form. After submitting the request form, a machine-readable CSV file format. Such datasets can an acknowledgment receipt is sent to the registered readily be used for scientific analysis and data processing, email address within 24 hr. Validation requirements (i.e. provided that the source is duly acknowledged and cited scanned valid ID, and research document if applicable) as indicated in the Terms and Conditions of Use set forth necessary for assessing the veracity of requests are also by PAGASA. Non-commercial use of climate data such asked from the requesting party. Nevertheless, the personal as those aimed for academic research purposes is provided free of charge, while applicable fees are charged for data aimed to be used in gaining profit or generating income (e.g. consultancy services). Redistribution of datasets acquired from ClimDatPh to third parties and data reuse for purposes other than the submitted documents is not allowed and, if deemed necessary, a memorandum of agreement or understanding might be executed. Each of the climate variables in ClimDatPh is available in terms of daily, monthly, and annual values covering a long period (from as early as 1949, see Appendix Table I). To facilitate data rescue and QC procedures, climate data are made available a year after the actual observations were taken. This means that the “present” as indicated in Appendix Table I is one year behind the current year. Also, requests for sub-daily observations, other climate variables (not included in Table 1), and locations not found in Appendix Table I are entertained through the ClimDatPh but depend on electronic data availability and may take longer processing time. Note that as mentioned earlier, the new database management system (PUMIS) is yet to be fully implemented. This means that additional stations (e.g. agrometeorological stations, automatic weather stations, and automatic rain gauges) and more climate variables (e.g. solar radiation, atmospheric pressure, visibility, sunshine duration, etc.) would also be made available in the future.

RESULTS AND DISCUSSION

Preliminary Statistics of Data Requests and Usage During the first four months of ClimDatPh’s existence on Figure 3. Illustration of the step-by-step procedures to be followed the internet (September–December 2019), it has catered in acquiring data from ClimDatPh. to various climate data needs. Data users in many parts of

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Figure 4. Preliminary statistics of data requests facilitated through the ClimDatPh based on a) request origin, b) request count, and c) climate data usage per sector. The statistics are taken from successful (claimed) data requests and only during its first four months of existence on the internet.

the world – including researchers from the United States, accessibility brought by ClimDatPh is expected to further the United Kingdom, Italy, Belgium, Switzerland, Japan, increase data request count to aid in uplifting research Australia, South Korea, Colombia, and – have been productivity and undertakings while utilizing PAGASA’s served by the platform (Figure 4a). In the Philippines, climate data. It should be noted that invalid requests the majority of climate data users served through the caused by a number of reasons (e.g. unavailability of data, ClimDatPh are from the National Capital Region, but incomplete request information, etc.) are not included in the platform has also catered those from other parts of the count statistics. Nevertheless, suggestions for data Luzon and enabled access to PAGASA climate data even users requesting for unavailable data are provided. For from the provinces of Visayas (e.g. , , , instance, solar radiation datasets are currently undergoing , ) to as far as the provinces in Mindanao data recovery and encoding, which makes it temporarily (e.g. , , , unavailable in ClimDatPh. Data users are referred to other ). It is further expected that ClimDatPh online platforms such as the National Aeronautics and will be able to reach data users from far-flung regions of Space Administration Prediction of Worldwide Energy the country in the future. Resources (POWER 2020), which contains near-real- time meteorology and solar-related datasets formulated In terms of user request count, ClimDatPh has served a for assessment and design of renewable energy systems. total of 462 successful climate data requests during its first four months being available on the internet as shown It is of great interest that aside from the weather and climate by the line graph in Figure 4b. Climate data requests community, which account for 16% of the purposes of data acquired in October and November of 2019 account to usage, the climate data provided through the ClimDatPh ~ 70% (319) of the total successful data requests, 207 were also requested for use in many other sectors (Figure of which are aimed for non-commercial purposes (e.g. 4c). The climate data were requested for investigating for students’ research requirements). On the other hand, problems related to water and hydrology (21%), energy there is an apparent decrease in data requests during the (14%), agriculture and fisheries (11%), environment and month of December, which is mainly due to fewer school biodiversity (10%), infrastructure and construction (6%), and office days caused by the holiday season. The online disaster risk reduction and management (DRRM) (5%),

59 Philippine Journal of Science Villafuerte et al.: Online Platform for Climate Vol. 150 No. 1, February 2021 Data Acquisition health and safety (5%), and soil and land use (4%). Other farming practices; it is applied separately for each quarter sectors (8% of the requests) that have benefited from and in each of the ecosystems. The monthly SPEI is also ClimDatPh include transportation, labor, tourism, finance, plotted in a time series (Figure 5c). Positive (negative) trade, and information communication technology. We SPEI values indicate rainfall surplus (deficit) in the expect that the knowledge of data available in ClimDatPh country. Notably, most of the rice production losses and the information on climate variables as described in this experienced in both ecosystems (large negative values study would likewise increase requests in applying climate in Figures 5a and 5b) coincide during drought events data to different sectors for their own research initiatives. (brown bars below 1σ marked by the horizontal dashed line in Figure 5c). To determine what might have caused such recorded droughts, the time series of monthly ONI Example Use Case of ClimDatPh: Investigating is likewise shown in Figure 5d. Positive (negative) values Climate Impacts on Rice Production of ONI, particularly above (below) 0.5 °C indicate El In this section, we present an application of ClimDatPh- Niño (La Niña). It can be noticed that drought events derived dataset in agriculture to provide an example of correspondingly occur during strong El Niño episodes. the possible use of the data available in ClimDatPh. In More remarkably, the 1997–1998 El Niño episode this example, climate data are converted into an index to caused a significant rainfall deficit, which then resulted better relate the impacts of climate-induced droughts on in a major rice production shortage across the country. rice production. It is encouraged that, if applicable, the These findings further confirm the widespread drought use of indices is adopted by future researchers who wish experienced in the Philippines from the last quarter of to utilize PAGASA’s climate data. We stress that showing 1997 to the first quarter of 1998 (Hilario et al. 2009), a comprehensive analysis of its application is beyond the and the earlier studies that demonstrated the impacts scope of this study, but rather more simply to illustrate of El Niño on rice production in the Philippines (e.g. the potential use of ClimDatPh in many other sectors. Lansigan et al. 2000; Dawe et al. 2009). Additionally, The susceptibility of the Philippines to droughts, which the earlier reported reduction in rice production for both can further be exacerbated by El Niño events (Reyes et irrigated and rainfed ecosystems in Luzon during El Niño al. 2009), leaves the country vulnerable to significant events (Roberts et al. 2009) has been extended in this rice production losses. To help alleviate the risks of study to the impact of El Niño on both ecosystems for extreme climate conditions such as El Niño, it is important the whole country. to understand how the country’s climate affects rice The ClimDatPh-derived data has proven useful in production. In this study, we derive the standardized explaining drought as a possible cause of rice production precipitation evapotranspiration index (SPEI) developed losses experienced in the country. Similarly, ClimDatPh by Vicente-Serrano et al. (2010) using the monthly climate data would also allow the research community to extend data from ClimDatPh to identify hydro-meteorological their respective studies to a wider range of environments, drought conditions experienced in the country. Here, the impacts, and possible adaptations. The available surface monthly values of RR, Tmin, and Tmax are used from 35 climate variables would also allow research activities on synoptic stations with a sufficient amount of data (missing historical and ongoing observation-based assessments values comprise < 5% during the period 1987–2018 of other extreme events and on epidemiological studies, corresponding to the period of available rice production which according to Trenberth et al. (2014) depends data). These 35 synoptic stations with sufficient data are largely on the data sources considered and the choice marked with white dots in Figure 2. El Niño events are of specific indices. identified from the Oceanic Niño 3.4 Index (ONI), which was downloaded from the NOAA Climate Prediction Center (NOAA-CPC 2020). Quarterly rice production data for both irrigated and rainfed ecosystems across all provinces in the country between 1987 and 2018 were CONCLUSION downloaded from the website of the Philippine Statistics As part of PAGASA’s initiative to help increase the Authority (PSA 2020). utilization of climate data in the Philippines, the present study describes an online platform for accessing the Figure 5 compares the rice production data and the country’s climate data called ClimDatPh. The method derived climate indices. The time series of detrended (i.e. of observation and definition of climate variables were the residuals of linear fit) quarterly rice production of discussed to serve as a detailed description of the climate the Philippines for rainfed and irrigated ecosystems are data provided through the online portal. Furthermore, shown in Figures 5a and 5b, respectively. Detrending is QC procedures following the WMO recommendations applied to the time series of rice production to minimize along with data requests and acquisition procedures were the influence of the introduction of new technology in

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Figure 5. Time series of detrended seasonal rice production in a) rainfed ecosystem and b) irrigated ecosystem aggregated across all the provinces of the Philippines. The time series of monthly SPEI is also shown in c) and the oceanic Niño 3.4 index in d). The “Q1” in a) and b) refers to the first quarter of the year (i.e. January–March), “Q2” as the second quarter (i.e. April–June), “Q3” (July–September), and “Q4” (October–December). detailed to allow data users to judge its usability for their country coincide with drought events that are primarily own intended applications. In its first few months of triggered by strong El Niño episodes. Through the existence, ClimDatPh has served various climate data sectoral example in agriculture, climate impacts were users from different sectors such as water, agriculture, investigated using ClimDatPh data. This can further health, and infrastructure, among others. Moreover, its generate informed decisions leading to relevant policy internet presence has allowed access to the climate data development. Ultimately, the online accessibility being of PAGASA from different parts of the country and across offered by the ClimDatPh could lead to increased the world, increasing its reach to contribute to global utilization of the Philippine climate data and further research endeavors. advance climate-related research undertakings in the country. It is recognized, however, that additional climate In this study, we also showed an example of a use case variables are needed to be included in the datasets. scenario of ClimDatPh. The monthly data of RR, Tmax, Climate-related information for areas not covered by and Tmin derived from ClimDatPh were used to compute the limited number of stations available in ClimDatPh SPEI to relate drought conditions with the extent of is also envisioned to be provided in the future. We rice production losses experienced in the Philippines. note that ClimDatPh is an initial step toward realizing It was found that rice production losses experienced these and further improvements are continuously being for both rainfed and irrigated rice ecosystems in the undertaken. Recent efforts include the involvement of

61 Philippine Journal of Science Villafuerte et al.: Online Platform for Climate Vol. 150 No. 1, February 2021 Data Acquisition the authors in developing a high-resolution gridded CECCATO P, RAMIREZ B, MANYANGADZE T, climate data that will combine in situ and remotely GWAKISA P, THOMSON MC. 2018. Data and tools sensed meteorological observations in the Philippines. to integrate climate and environmental information into public health. Infectious Diseases of Poverty 7:126. CREAYLA C, GARCIA F, E. 2017. Next Day Power Forecast Model Using Smart Hybrid En- ACKNOWLEDGMENTS ergy Monitoring System and Meteorological Data. We thank the men and women of DOST–PAGASA for Procedia Computer Science 105: 256–263. painstakingly and continuously gathering data from DAWE D, MOYA P, VALENCIA S. 2009. Institutional, the stations, transmitting them to the PAGASA-CO, policy and farmer responses to drought: El Niño events conducting the QC procedures, and many other tasks and rice in the Philippines. Disasters 33: 291–307. needed to ensure the accuracy of the climate data. We especially thank Engr. Nolan Rosel, Mr. Azmi Layugan, [E.O. No. 2] Office of the President of the Philippines, and Ms. Krista Coronel for providing the detailed Executive Order No. 2. 2016. Operationalizing in the information on the QC procedures being done at the executive branch the People’s constitutional right MGSS; Mr. Rex Abdon Jr. for explaining the details to information and the state policies of full public behind the PUMIS internal QC; and to Mr. Bernardo disclosure and transparency in the public service and David for the information he provided on the near-real- providing guidelines therefor. Malacañan Palace, time QC procedures being done at the Weather Division. , Philippines The internal review conducted by the PAGASA’s REMIA HILARIO F, DE GUZMAN R, ORTEGA D, HAYMAN P, Committee on the earlier version of this manuscript ALEXANDER B. 2009. El Niño Southern Oscillation is also acknowledged. We are also thankful for the in the Philippines: impacts, forecasts, and risk manage- careful review and helpful suggestions provided by ment. Philippine Journal of Development. 36: 9–34. the anonymous reviewers that led to the significant improvement of this article. LANSIGAN FP, DE LOS SANTOS WL, COLADILLA JO. 2000. Agronomic impacts of climate variability on rice production in the Philippines. Agriculture, Ecosystems and Environment, 82: 129–137. REFERENCES LI W, ZHANG P, YE J, LI L, BAKER P. 2011. Impact of ARGUELLES V, FORONDA J, INOBAYA M. 2019. Epi- two different types of El Nino events on the Amazon demiological and clinical comparison of influenza virus climate and ecosystem productivity. Journal of Plant infections including meteorological parameters affect- Ecology 4: 91–99. ing influenza activity in the Philippines, 2006–2012. LONGMAN RJ, GIAMBELLUCA TW, NULLET MA, International Journal of Infectious Diseases 79: 95. FRAZIER AG, KODAMA K, CRAUSBAY SD, BIAVETTI I, KARETSOS S, CEGLAR A, TORETI A, KRUSHELNYCKY PD, CORDELL S, CLARK MP, PANAGOS P. 2014. European meteorological data: NEWMAN AJ, ARNOLD JR. 2018. Compilation of contribution to research, development, and policy sup- climate data from heterogeneous networks across the port. Proceedings of SPIE 9229, Second International Hawaiian Islands. Sci Data 5:180012. Conference on Remote Sensing and Geoinformation MATSUSHITA N, NG C, KIM Y, SUZUKI M, SAITO of the Environment (RSCy2014). N, ARIYOSHI K, . . . HASHIZUME M. 2017. The BRUNET M, SALADIÉ O, JONES P, SIGRÓ J, AGUI- non-linear and lagged short-term relationship between LAR E, MOBERG A, LISTER D, WALTHER A, rainfall and leptospirosis and the intermediate role of ALMARZA C. 2008. A case-study/guidance on the floods in the Philippines. PLoS Negl Trop Dis 12: 1–13. development of long-term daily adjusted temperature [NOAA] National Oceanic and Atmospheric Administra- datasets. World Meteorological Organization WCD- tion. 2019. NNDC climate data online. Retrieved on MP-66/WMO-TD-1425. Geneva. 01 Oct 2020 from https://www7.ncdc.noaa.gov/CDO/ CARVAJAL T, VIACRUSIS K, HERNANDEZ L, HO H, cdoselect.cmd?datasetabbv=GSOD&countryabbv=&g AMALIN V, WATANABE K. 2018. Machine learning eoregionabbv= methods reveal the temporal pattern of dengue inci- [NOAA-CPC] National Oceanic and Atmospheric Admin- dence using meteorological factors in metropolitan Ma- istration – Climate Prediction Center. 2020. Monthly nila, Philippines. BMC Infectious Diseases 18: 1–15. Atmospheric and SST indices. Retrieved from 07 Feb 2020 from https://www.cpc.ncep.noaa.gov/data/indices/

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POPKIN G. 2019. Setting your data free. Nature 569: J, WILLETT KM, BENOY M, BRONNIMANN 445–447. S, CANZIANI PO, COLL J, CROUTHAMEL R, COMPO GP, CUPPETT D, CURLEY M, DUFFY [PSA] Philippine Statistics Authority OpenSTAT. 2020. C, GILLESPIE I, GUIJARRO J, JOURDAIN S, Palay and corn: area harvested by ecosystem/croptype, KENT CE, KUBOTA H, LEGG TP, LI Q, MATSU- by quarter, by semester, by region, and by province, MOTO J, MURPHY C, RAYNER NA, RENNIE JJ, 1987–2019 by ecosystem/croptype, geolocation, RUSTEMEIER E, SLIVINSKI LC, SLONOSKY V, year and period. Retrieved on 07 Feb 2020 from SQUINTU A, TINZ B, VALENTE MA, WALSH S, http://openstat.psa.gov.ph/PXWeb/pxweb/en/DB/ WANG XL, WESTCOTT N, WOOD K, WOODRUFF DB__2E__CS/0022E4EAHC0.px/?rxid=bdf9d8da- SD, WORLEY SJ. 2017. Toward an integrated set of 96f1-4100-ae09-18cb3eaeb313 surface meteorological observations for climate science [POWER] Prediction of Worldwide Energy Resource. and applications. Bulletin of the American Meteoro- 2020. POWER Data Access Viewer. Retrieved on logical Society 98(12): 2689–2702. 07 Feb 2020 from https://power.larc.nasa.gov/data- TONGSON E, ALEJO L, BALDERAMA O. 2017. Simu- access-viewer/ lating impacts of El Niño and climate change on corn [R.A. No. 10692] Republic of the Philippines, Republic yield in , Philippines. Climate, Disaster and Act No. 10692. 2015. The PAGASA Modernization Act Development Journal 2: 29–39. of 2015. Congress of the Philippines, , VAN DEN BESSELAAR EJM, KLEIN TANK AMG, Philippines. VAN DER SCHRIER G, ABASS MS, BADDOUR REYES C, MINA C, CREAN J, DE GUZMAN R, PAR- O, VAN ENGELEN AFV, FREIRE A, HECHLER P, TON K. 2009. Incorporating regional rice production LAKSONO BI, JILDERDA IR, FOAMOUHOUE AK, models in a simulation model of rice importation: A KATTENBERG A, LEANDER R, GUINGLA RM, discrete stochastic programming approach. Philippine MHANDA AS, NIETO JJ, SUNARYO, SUWONDO Journal of Development 36: 101–131. A, SWARINOTO YS, VERVER G. 2015. International Climate Assessment & Dataset: climate services across ROBERTS MG, DAWE D, FALCON WP, NAYLOR RL. borders. Bulletin of the American Meteorological 2009. El Niño-Southern Oscillation impacts on rice Society 96: 16–21. production in Luzon, the Philippines. J Appl Meteor Climatol 48: 1718–1724. VICENTE-SERRANO SM, BEGUERIA S, LOPEZ- MORENO JI. 2010. A multiscalar drought index [SACA&D] Southeast Asian Climate Assessment & sensitive to global warming: The standardized pre- Dataset. 2020. Retrieved on 10 Jul 2020 from http:// cipitation evapotranspiration index Journal of Climate sacad.database.bmkg.go.id/ 23: 1696–1718. SEPOSO XT, DANG TN, HONDA Y. 2015. Evaluating VILLAFUERTE MQ, JUANILLO EL, HILARIO FD. the effects of temperature on mortality in Manila City 2017. Climatic insights on academic calendar shift in (Philippines) from 2006–2010 using a distributed lag the Philippines. Philippine Journal of Science 146(3): nonlinear model. Int J Environ Res Public Health 267–276. 6842–6857. [WMO] World Meteorological Organization. 2018a. SEPOSO XT, DANG TN, HONDA Y. 2017. Exploring the Guide to Climatological Practices. Report No. 100. effects of high temperature on mortality in four cities Geneva, Switzerland. in the Philippines using various heat wave definitions in different mortality subgroups. Global Health Action [WMO] World Meteorological Organization. 2018b. 10:1, 1368969. Guide to Meteorological Instruments and Methods of Observation. Report No. 8. Geneva, Switzerland. SMITH A, LOTT N, VOSE R. 2011. The integrated sur- face database recent developments and partnerships. BAMS 704–708. TRENBERTH KE, DAI A, VAN DER SCHRIER G, JONES PD, BARICHIVICH J, BRIFFA KR, SHEF- FIELD J. 2014. Global warming and changes in drought. Nature Climate Change 4: 17–22. THORNE PW, ALLAN RJ, ASHCROFT L, BROHAN P, DUNN RJH, MENNE MJ, PEARCE PR, PICAS

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Appendix Table I. List of the stations that are made available in ClimDatPh.

No WMO Station name Administrative Latitude Longitude Elevation Available record Documented index province (°N) (°E) (masl) changes/remarks No. 1 98429 NAIA Metro Manila 14.50470 121.00475 21.063 Jan 1, 1949 – No rainfall present observations from 1993–2010 2 98425 Port Area Metro Manila 14.58841 120.96786 15.0 Jan 1, 1961 – present 3 98430 Science Metro Manila 14.64507 121.04428 42.0 Jan 1, 1961 – Garden present 4 98328 16.40400 120.60154 1500.18 Jan 1, 1949 – present 5 98325 16.08682 120.35205 2.0 Jan 1, 1951 – present 6 98223 18.18308 120.53474 5.4 Jan 1, 1951 – present 7 98222 17.57500 120.38670 33.0 Jan 1, 1951 – Station transferred Aug 2004 from in Sep 2004 17.89012 120.45973 57.620 Sep 1, 2004 – present 8 98232 18.36017 121.63039 3.6 Jan 1, 1951 – present 9 98134 Basco 20.42728 121.97053 167.0 May 7, 2001 – Relocated from its present old location with lower elevation (11.0 m) in 2001 10 98133 Calayan Cagayan 19.26300 121.46700 13.0 Jan 1, 1961 – Temporarily closed present during the periods 1971–1986; 1989– 1990; 1992–1993; and 1999–2000 11 98132 Batanes 20.78696 121.83837 124.0 Nov 1, 1965 – Temporarily closed present from 2001–2010 12 98233 Cagayan 17.64773 121.75849 60.2 Jan 1, 1951 – present 13 98334 Baler 15.74880 121.63202 178.2 Jan 1, 1995 – Temporarily closed present from Aug 2004 to Aug 2005 14 98330 15.47038 120.95114 28.4 Jan 1, 1951 –Dec Temporarily closed 31, 2018 from 1981–1990; relocated to CLSU in 2019 15 98336 Casiguran Aurora 16.26508 122.12883 5.9 Jan 1, 1951 – present 16 98327 Clark 15.18222 120.56166 154.821 May 1, 1997 – present 17 98426 Subic 14.79193 120.27061 18.13 Sep 1, 1994 – present 18 98324 Iba Zambales 15.32615 119.96902 5.110 Jan 1, 1951 – present 19 98435 Alabat Quezon 14.10544 122.01767 5.5 Jan 1, 1957 – No data in 2011 present 20 98432 Ambulong 14.08766 121.06238 14.3 Jan 1, 1951 – present

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No WMO Station name Administrative Latitude Longitude Elevation Available record Documented index province (°N) (°E) (masl) changes/remarks No. 21 98434 Infanta Quezon 14.74828 121.67788 3.1 Jan 1, 1951 – present 22 98428 Sangley Point 14.49495 120.90683 3.0 Aug 5, 1974 – present 23 98433 Tanay 14.58122 121.36927 646.1 Oct 1, 1999 – present 24 98427 Quezon 14.01836 121.59656 157.7 Jan 1, 1971 – present 25 98431 Oriental 13.41458 121.18694 42.8 Jan 1, 1951 – Mindoro present 26 98526 Coron 12.00354 120.20001 59.9 Jan 1, 1951 – present 27 98630 Cuyo Palawan 10.85411 121.00816 4.0 Jan 1, 1951 – present 28 98618 Puerto Palawan 9.74013 118.75861 14.90 Jan 1, 1951 – Princesa present 29 98536 Romblon 12.57864 122.27034 176.55 Jan 1, 1951 – present 30 98531 San Jose Occidental 12.35968 121.04790 3.0 Jan 1, 1981 – Mindoro present 31 98440 Camarines 14.12860 122.98255 3.92 Jan 1, 1951 – Norte present 32 98545 Juban 12.83942 123.99698 16.4 Aug 1, 2010 – present 33 98444 Legaspi 13.15064 123.72841 15.696 Jan 1, 1951 – present 34 98543 Masbate 12.36632 123.62921 10.0 Jan 1, 1951 – present 35 98446 Virac 13.57790 124.20787 33.7 Jan 1, 1951 – present 36 98538 Roxas 11.60024 122.74969 2.768 Jan 1, 1951 – present 37 98644 Bohol 9.58420 123.81600 49.0 Apr 22, 2013 – Station was present transferred here from , Bohol in 2013 38 98642 9.33544 123.30334 8.000 Jan 1, 1951 – present 39 98646 Mactan Cebu 10.32232 123.98011 24.3 Aug 1, 1972 - present 40 98553 Eastern 11.66083 125.62861 3.058 Jan 1, 1951 – Closed starting Apr Mar 31, 1987 1, 1987 11.66004 125.44228 2.4 Jan 1, 2001 – Re-opened here in present 2001 41 98546 Catarman 12.50537 124.62851 5.78 Jan 1, 1951 – present 42 98548 Western Samar 11.77502 124.88425 5.0 Jan 1, 1951 – present 43 98558 11.04558 125.75549 60.0 Aug 1, 1973 – present

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No WMO Station name Administrative Latitude Longitude Elevation Available record Documented index province (°N) (°E) (masl) changes/remarks No. 44 98648 10.13900 124.86040 72.0 Jul 18, 1972 – No data from Oct present 1, 1976 to Dec 31, 1977 45 98550 Leyte 11.22555 125.02500 2.7 Jan 1, 1951 – Relocated to its Nov 2013 current location after Yolanda’s devastation in Nov 2013 11.24348 125.00784 2.7 Aug 1, 2014 – present 46 98741 Zamboanga del 8.59957 123.34372 3.7 Jan 1, 1981 – Norte present 47 98836 Zamboanga Zamboanga del 6.91709 122.06631 6.9 Jan 1, 1951 – Sur present 48 98747 El Salvador Misamis 8.53570 124.55794 8.902 Nov 01, 2013 – Relocated to its Oriental present current location from Lumbia, 49 98751 8.15142 125.13385 689.3 Jan 1, 1951 – present 50 98753 Davao Davao Del Sur 7.12757 125.65496 18.0 Jan 1, 1951 – present 51 98851 South 6.05734 125.10314 132.199 Jan 1, 1951 – present 52 98752 Agusan Del 8.94708 125.48229 17.7 Jan 1, 1981 – Norte present 53 98755 Surigao Del Sur 8.36746 126.33850 3.0 Jan 1, 1951 – present 54 98653 Surigao Surigao Del 9.78279 125.48935 39.27 Jan 1, 1951 – Temporarily closed Norte present from Jan 1, 1979 to Jan 7, 1984 55 98746 Cotabato 7.16172 124.21480 44.900 Feb 01, 1986 – present

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