Frequently Asked Questions (FAQ’s) on the Automated Weather Stations (PL480 Project)
These FAQs were developed to facilitate the response to the questions that are frequently asked about the Automated Weather Stations (AWS) which is the major component of the PL480 Project, a NAFC-assisted project that has been approved for implementation by WRMD starting this CY 2012 and will be completed by 2014. This also aims to have a common understanding of all the aspects of the project specifically AWS.
Question 1: What is AWS?
Answer: AWS or Automated Weather Station is compact equipment powered by storage battery and solar cells, which are more accurate and dependable instruments for collecting climatic data. The AWS to be established by the project is capable of collecting 10 weather parameters namely:
- Wind speed and direction
- Rainfall amount and intensity
- Air Pressure
- Relative humidity
- Solar Radiation
- Sunshine Duration
- Soil Temperature
- Soil Moisture
These instruments are equipped with data storage system, thus data downloading can be done on a daily, weekly or monthly basis. The frequency of data collection can also be programmed according to the needs and use of such information. Moreover, a telemetry system could be connected to the AWS so that data can be remotely accessed through mobile SMS or other form of messages in real time.
Question 2: What is the difference between AWS and existing Agromet Station?
Answer: AWS is an automated version of the traditional Agromet station, either to save human labor or to enable measurements from remote areas. An AWS typically consists of a perimeter fence, box enclosure containing the data logger, rechargeable battery, telemetry and the meteorological sensors with an attached solar panel. Data from the AWS are transmitted in real time to a central server which can be viewed from a web-based system through internet. While the data from the existing agromet station are recorded daily in a prescribed form from PAGASA by an assigned agromet observer and are submitted to BSWM every month through mail.
Question 3: Why the project preferred AWS developed by the ADVANCED SCIENCE AND TECHNOLOGY INSTITUTE (ASTI) of DOST than other commercially available and much cheaper brands?
Answer: The AWS developed by ASTI-DOST is not only a technology or an instrument; it is a system that consists of data generation, archiving and remote transmission of real-time data. More important, AWS of ASTI complies with the WMO quality standard according to PAGASA; and likewise support the research and development program of DOST.
The AWS of ASTI has the following unique features and characteristics:
- ASTI is using a multi-parameter weather sensor from Lufft which is recognized by PAGASA and WMO; the sensors alone cost about P160,000.00. While the commercial AWS usually has only 6 measuring weather sensors.
- It is capable of measuring nine (9) parameters/sensors that include: rainfall amount and Intensity, wind speed and direction, temperature, relative humidity, air pressure, solar radiation, sunshine duration, soil moisture and soil temperature and evaporation.
- It is powered by a solar panel and storage battery and the data transmission is through GSM Network or satellite. Data transmission is real time and all data will be stored and archived in central server with back-up.
- Gathered data maintenance is web-based and accessible through the internet.
- With regard to WMO Standard for measuring weather data especially for wind parameters requiring 10 m pole high, the sensor used by ASTI provides the data at 2 meter pole high for agromet station.
- The Engineering and Technical Services Division of PAGASA accepts the data from ASTI deployed AWS.
- The life span of AWS’ sensors is 13 to 15 years.
- The sensors are pre-calibrated prior to assembly and can be used immediately upon installation; for recalibration every two (2) years.
Question 4: What is the level of coordination with PAGASA?
Answer: BSWM and PAGASA through their respective Departments (the DA and DOST) have entered into a Memorandum of Agreement (MOA) on May 18, 2010 purposely to strengthen BSWM’s capability in handling agro meteorological and AWS, and for PAGASA to assist the BSWM in the training of agro meteorological and AWS observers, installation of AWS and calibration and maintenance of equipment.
From the onset of the project’s conceptualization, PAGASA is always being consulted with regard to technical matters and closely coordinated particularly in site selection (i.e. not within the PAGASA’s present and future plans. Moreover, there are four (4) technical staff from PAGASA that were allowed to provide assistance during the entire duration of the project through a Special Order issued by PAGASA.
Question 5: Does the AWS capable of forecasting?
Answer: The AWS will generate data base on different weather parameters. The AWS can be used as a tool for simple farm level weather forecasting within the locality. For instance, if the real-time readings from AWS for relative humidity is 90% and above and the pressure reading is 1008 hPa and below with other visual indications such as formation of clouds, then it can be predicted that rainfall event will occur within the day. After generation of long term data say about 5 years, these can be analyzed for predicting weather pattern for a specific locality.
In so far as large-scale and longer term forecasting are concerned, it is PAGASA as the authorized weather agency of the country that is responsible and has the skills and capability to conduct weather forecasting using all the available data from global, regional, national and local sources.
Question 6: How the farmers would benefit from AWS?
Answer: The benefits of AWS can be derived through the application and utilization of weather data to be generated, both real time and historical, to their farming activities. For this project, a Climate Field School (CFS) will be conducted for each AWS site.
The module for this CFS consists of topics geared toward understanding the basic principles of different weather and climatic elements and how the values of these parameters affect crop production at a specific growth stages. Cognizant of the importance of weather to crop production, it is but rational to integrate the module of CFS to the existing Farmer Field School (FFS). Through this school, the farmers will learn by discovery and doing by themselves.
They will be provided with knowledge and skills on diagnosing the impact of extreme weather event e.g. high temperature to the occurrence of certain pests or diseases that are harmful to their crops. Through this learning, they will be prepared to make informed decisions on how these pests or diseases can be prevented or cured.
|Immediate/Short Term||Long Term|
|Recording site-specific weather conditions for agromet researches and routine weather monitoring||Maintain yield and/ or avoid losses thru on-site tactical application, say controlling conditions in a greenhouse||Ensure increased yield thru better choice of crops and crop varieties and technologies to suit specific site physical features and agro-climatic conditions|
|Collection of local and regional (i.e. thru a network of AWS) real-time weather information for forecasting, local warning, and hydrologic analysis||Avoid damage and losses by deployment of early warning system in a larger scale (e.g. River basin)||Increased yield and/or reduced losses thru appropriate cropping pattern and calendar on a regional scale using reliable climate info and forecasts. Efficient and properly designed irrigation system.. Avoid losses thru agro-weather forecasts & advisories|
|Collection of high degree of details of weather information in terms of time and spatial variability and trend for accurate forecasting||Reliable and realistic short term decisions for efficient farming operations. Better monitoring of environmental conditions and occurrence of pest and diseases||More precise calculation of irrigation requirements to allow more accurate estimates of potential irrigated areas and available water. |
Better forecast to forewarn farmers and undertake necessary precautions.
|Weather-based crop insurance application||Assured just compensation against crop damage thru improved calibration of crop losses due to abnormal weather conditions||Better development of rainfall distribution index for crop insurance|
|Crop management decision – such as irrigation scheduling (thru field water balance) and integrated pest management||Better control and management of water delivery and water use. Biological observation and correlation of pest occurrence with weather condition||Preparation of better crop water budget and irrigation scheduling to optimize use of available water Reduced impacts of pest and diseases|
|For obtaining weather data collectively and accurately and calculation of agromet products on-board (e.g. evapotranspiration)||Immediate availability of crop consumptive use or evapo- transpiration data||More dependable and reliable data on crop water requirement for the design of irrigation system and for monitoring crop growth|
|Disaster risk reduction and early warning system deployment||Reduced damage and losses thru early warning and advisories||Facilitate cropping pattern and calendar adjustment to avoid impact of extreme events. |
Provide strategic (e.g. relocation of farm) and tactical management decisions (i.e. evacuate and save farm assets).
Question 7: How the sites for AWS were selected?
Answer: Site selection for the installation of AWS is very critical, hence, a general site criteria for the project was first developed. With these criteria, the DA-RFUs were requested to submit a list of possible sites where the AWS can be installed. During the Consultative Workshop last February 21-24, 2012 where the assigned Focal Persons from all regions are present, the list of AWS sites were finalized. However, these final lists per region are still subject for validation following the specific site criteria provided by PAGASA as shown below:
- Site is fairly level and free from obstruction
- Site has a grass cover and no tall weeds
- Site is not concrete, asphalt or crushed stone
- Obstruction such as trees, buildings and nearby shrubs is not close to the instruments; distance of AWS should be at least 4 times the height of obstruction
- No obstruction can cast shadows during the greater part of the day, though brief periods of shadow near sunrise and or sunset are sometimes unavoidable; hence the east-west direction should be identified
- Site is accessible from all means of transportation for operation and maintenance
- Site should be located in a place truly representative of the natural conditions in the agricultural region
- Recipient (DA-RFU-ROS, LGUs, SUCs) is willing to provide observer for manual reading of standard rain gauge and for overseeing the operation and maintenance of installed AWS
- With strong GSM signal (e.g. Globe, Smart, Sun)
- Not visited by flood annually
- No social/right of way problem; if the recipient is not the land owner, usufruct agreement from the owner is mandatory
- Security of the site is outmost concern
Question 8: How AWS can be used for Early Warning and Disaster Risk Management?
Answer: One of the calamities where local data from AWS can be tapped for early warning system development is flooding. There are already cases in the past where a community-based early warning system for floods was based on established values of rainfall, river water level and soil moisture. Absolute real-time values of rainfall and other weather elements may not be useful without a pre-established base data.
To be able to develop an early warning system in a locality, we need to understand first the bio-physical condition or characteristics of the environment e.g. soil type, vegetation, prevailing season, and other historical events. There is a need for data monitoring and recording of extreme events, knowledge of normal values of rainfall and other parameters prior to development of early warning system. Once the system or the values were established, the real-time values from the AWS can be used as an indicator that flooding would have the probability of occurrence. With this early warning system, the risk of losing lives and properties in the locality may be minimized if not totally prevented.
On the other hand, the cumulative or historical data from the AWS e.g. 10 years can be used for estimating the probability of occurrence using appropriate statistical models of high or low rainfall events in a year that may cause flooding or otherwise.
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