case study weather monitoring system

Weather Reporting System Using IoT: Benefits & Use Cases

Imagine a world where the weather is no longer an unpredictable force that catches us off guard. This reality is already here, owing to IoT based weather monitoring systems . How do they work, which industries do they serve, and what benefits do they offer?

This article has you covered as we dive into these and even more questions. Learn how IoT technology allows businesses to adapt to changing weather conditions and optimize performance with WebbyLab, an experienced IoT development vendor .

We’ll navigate you through the applications of weather monitoring system using IoT and explain the importance of such ventures. Drawing from our weather reporting expertise in projects like 2Smart Standalone , we’re here to elevate your business operations through the Internet of Things .

Weather forecasting scenario in the 2Smart Cloud platform.

The Role of IoT in Weather Reporting Systems

Traditional weather monitoring systems are getting outdated. They’re often prone to errors and inaccurate predictions — and these factors can damage your business. But with IoT in place, these systems transform dramatically. Here’s how.

• Traditional weather reporting methods, while informative, often suffer from delays in data collection and transfer. IoT, in turn, gathers data from sensors in real time.
• IoT takes forecasting to the next level when combined with internet-connected weather stations. It can predict the weather accurately and quickly for different geographic locations.
• IoT-based weather monitoring systems seamlessly integrate data from a multitude of sources. Weather sensors , satellite imagery, weather stations, drones, and even personal devices are just some examples.
• The Internet of Things brings precision to weather insights. Businesses can collect data from specific places, e.g., by improving crop watering based on soil moisture or changing flight paths to avoid turbulence.

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Benefits of Weather Reporting System Using IoT

With the Internet of Things, weather monitoring systems have stepped up their game, becoming more accurate, consistent, and quicker in operation. Now, that sounds great, but what does this tech actually offer businesses? Here are the main benefits:

• Real-time data collection. Thanks to IoT devices and sensors, businesses get weather updates from all sorts of locations right when they need them.
• Higher accuracy. A weather monitoring system using IoT gathers data from numerous sources, providing a detailed picture of what’s happening with the weather.
• Wider coverage. Traditional weather monitoring systems usually check conditions at just a few weather stations, leaving out other areas. When IoT technology steps in, businesses can cover more places, even the most distant ones.
• Predictive analytics. IoT-powered weather reporting systems use historical data and current trends to tell businesses what’s coming. That’s how companies can adjust their operations before any weather-related challenges appear.
• Reduced response time. Every second counts when the industry is about making fast decisions — aerospace or emergency services. That’s where IoT weather monitoring comes in as a solution , offering real-time insights for rapid response times.
• Cost-effectiveness. A weather reporting system using IoT allows companies to optimize resource usage and reduce waste. For example, agriculture businesses can leverage weather forecasts to adjust their irrigation and crop protection measures.
• Increased safety. Industries prone to weather-related risks, like construction or transportation, enjoy the advantages of IoT-based automatic weather stations . These businesses can plan their activities more safely with a minimum of accidents and disruptions.

Industries That Benefit from Weather Monitoring System Using IoT

Now that you know the main benefits that IoT systems bring to weather monitoring, let’s dive into the industries that reap these advantages. Here are several examples:

Manufacturing

Manufacturing processes are intricately linked to the weather. IoT technology ensures businesses time their production just right, coordinating with the best weather conditions. Whether it’s managing temperature-sensitive materials or adjusting supply chain logistics based on weather forecasts, manufacturing gains efficiency and precision. 

Flying safely and smoothly is the core of aviation, and the weather here is a big player. Thanks to IoT-based weather reporting, airlines and air traffic control get real-time updates on turbulence, lightning, and visibility — all necessary to make flights safer.

Agriculture

Imagine having a trusted partner in farming decisions — that’s what IoT weather insights are all about. When it’s time for planting, watering, or dealing with pests, these become essential. Farmers can get information on rainfall, temperature, and soil moisture with IoT to make smarter choices for their fields.

IoT based weather monitoring systems are handy in the automotive industry. When the weather gets worse and the roads get slippery, the Internet of Things may help. It assists in developing advanced driver assistance systems and fine-tuning cruise control based on visibility, ultimately adapting vehicles for safer driving.

Warehouse Management

Inventory management and logistics greatly depend on the weather. And that’s where IoT weather stations come into play. They help warehouses predict when severe weather might influence their goods and adjust storage conditions accordingly. The same goes for shipping schedules.

The energy sector, whether using renewable or non-renewable sources, is influenced by weather patterns. Using IoT energy management systems, this industry can predict when people will need more or less energy, ultimately optimizing resource allocation and enhancing grid stability.

Weather conditions are crucial for insurance companies. It’s a common instance in the US, where numerous insurance categories exist — from property protection in case of floods and hurricanes to evaluating risks for energy facilities, agriculture, health, and more. With IoT, insurers can assess risks and price their services properly.

Types of Sensors Used for Weather Monitoring

When learning how to develop IoT-based weather reporting system and considering deploying one, the sensors you choose depend on what you’re aiming to explore. There are various gadgets, each with its own particular purpose. Look at how different sensors work for various scenarios:

Boosting Crop Yields

If farmers are diving into smart agriculture, they need information on temperature, humidity, soil moisture, and rainfall. Here are the sensors used in such a case:

• Temperature sensor
• Humidity sensor or hygrometer
• Soil moisture sensor
• Rain sensor

Increasing Flight Safety

Think about pilots preparing for a smooth flight. They need to know all about wind speed, direction, atmospheric pressure, and even visibility. Check out the sensors suitable for this use case:

• Barometric sensor
• Visibility sensor

In a nutshell, the sensors you pick for weather monitoring depend on what you’re diving into.

Reasons to Invest in an IoT-Based Weather Reporting System

Whether in healthcare, agriculture, aviation, transportation, automation , warehouses, or laboratories, the right weather information is your compass. And here are the primary reasons to invest in a weather monitoring system using IoT :

• Continuous climate monitoring. An IoT-based weather reporting system tracks all environmental changes, whether small or big. It means you always know what’s happening and can prepare for unfavorable weather beforehand.
• Preventing losses. The IoT platform for weather monitoring serves as your early warning signal. It allows you to minimize damage and secure your business.
• Avoiding business disruptions. With IoT in place, you don’t have to shut down your business even if the weather gets rough since you’re ready to adapt. You can adjust schedules, reroute logistics, and ensure smooth operations, leveraging predictive analytics and real-time insights.

WebbyLab Experience in Weather Monitoring Using IoT

WebbyLab has a profound background in leveraging the Internet of Things for weather reporting. Here’s our case study on IoT system for weather monitoring :

Leveraging Ready-Made Solutions

We understand that building custom weather stations from scratch might seem the way to go, but we’ve taken a different route. Recognizing the extensive resources and time invested in refining and testing existing solutions, we’ve opted to work with these well-established options. This decision lets us focus on what truly matters — extracting accurate and reliable weather data.

Using Modern Devices

In today’s market, especially in industrial contexts, weather station devices are often designed for large ecosystems. Their software isn’t necessarily tailored to specific clouds or applications. To bridge this gap, we employ separate gateways. These gateways allow us to run our software, connect physically to devices through interfaces like RS-485, RS-232, One Wire, or Ethernet, and process the collected data. Owing to that, we can integrate weather insights into our solutions easily.

Integrating Weather Monitoring into 2Smart Standalone and 2Smart Cloud

Ease of access and usability are critical for us. That’s why integrating weather data into our 2Smart Standalone platform has been a priority. We’ve established integrations with virtual weather platforms like OpenWeather and YahooWeather. With these in place, users can create virtual devices effortlessly and receive real-time weather data for their locations.

The virtual weather stations in the 2Smart Standalone platform.

The same goes for our 2Smart Cloud platform , where anyone can set up a weather station and access weather information via our mobile application.

The weather station in the 2Smart Cloud platform.

Tailoring Weather Insights for Specific Needs

We’ve also tailored weather insights to specific applications. Our smart greenhouse solution, built on the 2Smart Standalone platform , incorporates a unique sensor-based weather forecasting scenario. Users can easily integrate their real-time pressure and wind direction data. Our algorithm then calculates the nearest weather forecast, enhancing precision for greenhouse operations.

Get Weather Insights with WebbyLab

If you’re tired of Mother Nature’s surprises and want to protect your business from weather-related disruptions, consider using an IoT based weather monitoring system . This solution allows for real-time data collection and unparalleled forecasting accuracy, ultimately optimizing your processes.

Ready to feel the difference between traditional weather monitoring and IIoT-based monitoring system ? Contact WebbyLab experts . With our profound experience in IoT projects , we’ll elevate your business with precise weather forecasting solutions.

Learn more about how we engage and what our experts can do for your business

Written by:

Kostiantyn Oliynyk

Head of IoT at Webbylab

With a robust academic background in Telecommunication Systems Engineering, I apply my knowledge to lead innovations in the IoT domain. Starting as the first team member in the newly formed IoT department at WebbyLab, I've spearheaded its growth, fostering the expansion into embedded and hardware development alongside our core software projects. My dedication lies in pushing the boundaries of IoT technology, fostering a culture of innovation and excellence that profoundly impacts our clients' operational success.

It uses sensors and devices that gather weather data, connect through messaging protocols , and transmit the information to a central hub or a cloud platform. The collected data is then analyzed to get up-to-date weather insights.

A weather monitoring system using Arduino can observe various parameters: temperature, humidity, air pressure, wind speed, wind direction, rainfall, soil moisture, UV radiation, and more.

Businesses can implement different security measures to protect their IoT weather monitoring systems. Some examples include encryption, robust communication protocols, authentication mechanisms, and adherence to the latest IoT standards .

Users typically get weather information through mobile apps, web interfaces, and dashboards.

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Weather monitoring system comparison: Which is right for you?

A comparison of common weather monitoring methods, pros and cons, and which technology might apply to different types of field research.

CONTRIBUTORS

As a researcher, you know the reasons for monitoring weather parameters at your research site are practically limitless. Unfortunately, the available options to make those measuremenrts are also limitless, which can be daunting when you want to know which  weather station  or weather monitoring system is right for your unique situation.

METER scientists have spent thousands of hours  installing weather stations , and monitoring, interpreting, and publishing data from field experiments. Over time, we’ve learned a great deal about how to obtain high-quality weather data. In this article, we share that expertise with you. Watch the video or read the article below for a comparison of common weather monitoring methods, pros and cons, and which technology might apply to different types of field research. Also learn why modern weather monitoring is about more than just the weather station.

Weather monitoring systems: The performance vs. price sweet spot

Figure 2 is a graph comparing weather station performance versus price. In an ideal world, a higher price would equal higher quality, and the price versus performance continuum would be a straight line. But choosing a weather station is not strictly about price versus performance. In Figure 2, the transverse axis is a “value” axis. If a weather station gives you a better price to performance ratio (meaning you can get exactly the right performance at a price you can afford), then that weather station will provide a higher value for your unique measurement needs.

Factors that affect the value of a weather monitoring system

The prices of the weather stations shown on the x axis in Figure 2 are set, so it’s only the y axis, or the performance of an instrument in your particular application that changes the value. Many different factors affect the relative performance of a weather monitoring system, such as:

• Installation and maintenance requirements Measurement suite
• Remote data acquisition
• Real-time data visualization
• 4-season capabilities
• Power requirements

Performance is defined by your unique measurement needs. For example, if you’re measuring in a remote location where you can’t access the site routinely, you’ll need extremely robust instrumentation. Robust weather monitoring equipment is also needed if people’s lives are at stake (i.e., if a sensor breaks and a flash flood isn’t detected, people’s lives could be in jeopardy). So in these situations the robustness of a weather monitoring system will drive the relative performance. Other scenarios might be as follows:

Scenario 1 : You may be a climatologist monitoring air temperature to study the effects of climate change. If so, you’ll need a continuous, accurate record of air temperature for several decades. In this case, both the accuracy and the stability of a weather station or weather monitoring solution is the driving factor that affects the performance relative to your measurement needs.

Scenario 2 : If you’re running a huge network of remote weather stations , and the cost of making a field trip for maintenance and installation is significant and actually dwarfs the cost of purchasing the equipment in the first place, then possibly the maintenance requirements of the instrument are what drive the performance for your application.

Scenario 3 : Researchers often need specialized measurements. You may need more than the typical weather measurements such as air temperature, humidity, and rainfall to answer the research question. In this case, the type of measurement suite containing the specialized measurements you need are what drives the performance of the weather station.

Scenario 4 : Some systems have three-season capabilities vs four-season capabilities. Four-season systems are heated and can function and give accurate results in high-latitude wintertime. If you’re studying wintertime precipitation you’ll need a heated rain gauge that can capture the snow precipitation. However if you’re doing an agricultural study, a four-season system won’t be important to you because plants aren’t growing.

Scenario 5 : Power requirements will be important if you’re monitoring at a remote location. You’ll need a battery-powered system with low power requirements, so you can reduce travel time and cost. Thus in a performance vs. value graph, any solutions that don’t meet your weather monitoring requirements move lower on the value axis, and any that do meet your requirements move higher on the value axis. For example, if a particular WMO-grade system needs so much routine maintenance you can’t scale your network, it may move lower on the value axis, as shown in Figure 3.

Or if you’re performing a water balance study, you’ll need a precision rain gauge. However, the more-expensive all-in-one weather station may only have a rudimentary rain gauge that measures sound generated by water droplets hitting a drum. This would drive the performance and value of this particular system down (Figure 4).

So once you determine your measurement needs and arrange the various systems on the value axis, you can see which ones are more valuable to you and where they are on the cost continuum. This enables you to make better- educated decisions about which system to use for your application.

Classes of weather stations

Below are definitions of the various types and classes of weather stations you might encounter in the marketplace.

Aviation class weather monitoring system

Aviation class weather monitoring systems are differentiated by their specialized observations.

That’s why in Figure 2, if you look at the performance versus price continuum, aviation systems occupy the upper right corner where both performance and price are very high (i.e., $200,000+). They may include, for example, a visibility and present weather sensor that shows the distance that a pilot can reasonably see, a ceilometer that tells cloud height, or an instrument that indicates freezing rain or ice buildup. These specialized measurements wouldn’t be found on most weather monitoring solutions, but they do drive the performance of aviation weather systems. Aviation systems also have specialized communications with VHF transmission and redundant phone systems. And, because human health and safety depend on these systems, they are extremely robust and include four-season performance (unless they are in the tropics). In addition, the accuracy of aviation systems is very high because most of these data are piped into the climatological record.

WMO class weather monitoring system

World Meteorological Organization (WMO)-compliant weather monitoring systems are often found in national weather networks in many countries. Also some medium-scale mesonets adhere to WMO recommendations and guidelines.

WMO weather monitoring systems require a tower for measurements at ten meters, and other measurements are made lower down in the atmospheric profile at two or three meters. WMO-compliant systems need four-season capabilities and require high accuracy because these data also feed into our climatological record. The cost of these systems is high: approximately $20,000-$50,000, plus there are significant maintenance requirements that drive up the yearly cost of operation. This means the cost is prohibitive for dense spatial networks.

Custom weather monitoring systems

Researchers often need custom weather monitoring systems with a measurement suite tailor made for the research question they’re trying to answer. Also some weather networks use custom weather stations with a measurement suite that satisfies the needs of their stakeholders. So in addition to measuring normal weather parameters, users may add things like:

• Infrared thermometer for measuring surface temperature NDVI
• Redundant rain gauges
• Net radiation for surface energy balance studies
• A dual eddy covariance system measuring isotopic ratios

There are almost an infinite number of measurements researchers can integrate into a data acquisition backbone. That’s why in the price versus performance continuum in Figure 1, these custom weather station systems are scattered along either axis. You will often see these weather monitoring systems used in non-mesonet and non-National Weather networks.

Science-grade all-in-one weather stations

In the last two decades, we have seen a proliferation of all-in-one weather stations. This means instead of piecing together weather stations with custom sensors integrated into a data acquisition backbone, many manufacturers now integrate the various weather sensors into a small-package all-in-one station.

There are different types of all-in-one weather stations available, which means you have many different measurement-suite and price-point options. All-in-one weather stations cost between $1000 and $5,000, depending on the measurement suite and if it’s a three-season vs. a four-season instrument. Advantages of all-in-one weather stations are that installation and maintenance are much less complex than custom or WMO weather monitoring systems. This makes them a good option for dense weather station networks. Often you will see WMO class stations make up the backbone of a network. And then all-in-one weather stations fill the spatial gaps between those WMO class stations for a denser network with much richer information. The drawback of all-in-one weather stations is that they cannot strictly follow the WMO recommendations because they only make measurements at one height. So all-in-one weather stations have their niche just as WMO stations have theirs.

Hobbyist-grade weather monitoring system

Hobbyist-grade weather stations for weather monitoring are typically built for homeowners and commercial buildings. These stations are not particularly robust and not well suited for research or long-term monitoring.

One advantage of these stations is that the data acquisition and communication system relays the information to a console for localized weather measurements at a house or place of business. An Amazon search will produce many of these types of weather stations in the search results.

Which weather monitoring system should you choose?

Explore the case studies below to learn how researchers and growers select the right scientific weather station for their particular application.

Case study: FAO 56 ETo for irrigated agriculture

Penman-Monteith reference evapotranspiration is a measurement commonly made in irrigated agriculture. The Penman-Monteith equation is the mechanistically based equation that quantifies the amount of evapotranspiration or water loss from either a grass surface or an alfalfa surface. For example, if you had a well-watered grass or alfalfa surface, you can plug weather variables into the equation to show how much water vapor you would lose to the atmosphere.

The measurement is generally used in high-dollar, irrigated agriculture such as vineyards and, and fruit trees, but it’s also used in center pivot applications for agriculture. Growers need to know the water balance (how much water has been lost or gained in the system) so they can replenish the net loss with irrigation water. So for this particular measurement, growers may need localized measurements at many different locations.

A grower typically doesn’t need a complex weather monitoring system to make this measurement. They need something easy to set up, easy to install, with low maintenance requirements, remote data access, and small battery usage. For example, the data logger in Figure 10 has just a small solar panel that will run this weather station indefinitely.

The key driver of choosing a weather monitoring system for FAO 56 reference evapotranspiration is the need for both solar radiation and precipitation. Growers need to know the amount of precipitation replenishing the water in the soil, and they need a solar radiation measurement for the Penman Monteith (FAO 56) reference evapotranspiration measurements. Some all-in-one stations don’t have both precipitation and solar radiation. However, the ATMOS 41 all-in-one weather station does measure both solar radiation and precipitation. So this it’s good choice for this kind of weather monitoring in the agricultural setting.

Figure 11 is a graph from the ZENTRA Cloud data management software that works with ZL6 data loggers.

ZENTRA Cloud automatically makes reference evapotranspiration measurements on a daily and a cumulative basis. It allows you to add the crop coefficients to convert from reference evapotranspiration into true evapotranspiration. This makes the ATMOS 41 all-in-one weather station, ZL6 data logger, and ZENTRA Cloud software a valuable turnkey system for growers.

Case study: Weather monitoring on Mt. Everest

Unlike the previous case study where ease of installation and maintenance for a few specialized measurements was important, Campbell Scientific, Inc. is involved with a project to engineer weather stations deployed on Mount Everest . One of those stations is the highest elevation weather station that is active in the world. The conditions on Mount Everest are extremely harsh, so the robustness of this station is the key factor that drives up the performance for this particular measurement need.

These Mount Everest stations are ultra robust, four-season stations because they constantly measure wintertime conditions. They include redundant specialized anemometers with coatings to shed ice and snow in case one freezes up. You can tell there’s redundancy in some of the other measurements as well. So this is not a project that was driven by price considerations. Robustness is the key driver because the cost of going up to maintain the weather monitoring system dwarfs the cost of the system by orders of magnitude.

Case study: Washington State AgWeatherNet

Washington State University runs the Washington State AgWeatherNet. Each green dot in Figure 13 is the location of an Ag weather Net tier-1 weather station. These stations are concentrated primarily in the agricultural regions of Washington State in apple orchards and other high-dollar crops that (along with California) feed much of the United States.

AgWeatherNet tier-1 weather stations have a measurement suite tailor made for the growers in this particular region. The AgWeatherNet ingests data from these stations and outputs a number of modeled parameters like disease models, pest models, frost prediction, and frost monitoring. These models are extremely valuable for the producers in the region, who actually pay for the system.

What’s interesting about AgWeatherNet is that even though it looks like a dense spatial network, these stations are many kilometers apart. So an accurate tier-1 station sitting in a valley might measure 2 °C different than those at an orchard at the top of the hill. This means if they continuously monitor temperature and humidity in the valley and give a prediction for a fungal disease, that prediction will be different from the reality at the top of the hill.

To solve this problem, the AgWeatherNet allows individual growers to purchase and install tier-2 systems (Figure 14).

Figure 14 shows an ATMOS 41 all in one weather station used in the AgWeatherNet. It doesn’t have the accuracy specs of the tier-1 stations, but the lack of accuracy at the point scale is almost inconsequential compared to the spatial difference in the weather parameters as you move away from the tier-1 sites. These tier-2 stations fill the gaps in tier-1 observations and AWN can then use artificial intelligence along with these observations to perform hyper-local predictions for the growers who put these stations in. This strategy has been successful at helping to predict mold, pest outbreaks, or frost events at a particular grower’s location. It is easy to see how each weather station type plays a key role in providing stakeholders with critical data for decision making.

Case study: Weather monitoring in Africa

It’s difficult to predict weather if you can’t even observe the weather. Outside of the country of South Africa, there are almost no weather monitoring systems on the whole continent of Sub-Saharan Africa. This has a lot of negative repercussions for weather prediction for crop insurance and for the African farmer. It’s one of the reasons it’s been difficult to get efficient farming practices adopted in Africa. To help solve this problem, METER has partnered with the Trans African Hydro Meteorological Observatory ( TAHMO ) to put 20,000 weather stations in Africa.

TAHMO had important considerations that drove the performance value of the weather stations they wanted to install. First of all, they needed a simple installation because the ground crews are not particularly skilled. They also need a low-maintenance weather station because In many regions of Africa, it is extremely difficult to make field visits because of civil unrest, political instability, and malicious activity. So routine maintenance trips to fields less than a year apart are difficult and very expensive.

The ATMOS 41 all-in-one weather station was engineered specifically with the TAHMO project in mind to be ultra-robust despite harsh weather with no moving parts that could break. TAHMO has now installed more than 500 of these weather stations in Africa and is, at this point, the largest operational weather network on the African continent. These stations have had approximately a 95% uptime while interestingly, the aviation weather systems in Sub Saharan Africa generally run at about 67% uptime.

Case study: Montana Mesonet weather monitoring systems

Typically in the U.S., the National Weather Service (a division of NOAA) puts out a network of weather monitoring systems spaced out across the country, and that data gets fed into forward-looking models that help predict the weather. Researchers are finding out that putting in a sparse network of very expensive weather monitoring systems has done really well. But the spatial gaps in those weather monitoring networks are a problem, especially for agriculture producers and ranchers. They need to know what’s happening where they are.

Mesonets present a practical solution for the need to fill in data gaps between large, complex weather monitoring systems. The Montana Mesonet currently has 57 weather stations interspersed throughout the state, and through partnerships with both the public and private sector, they’re adding more stations every year. At each location, the Montana Mesonet team installs METER all-in-one weather stations , soil moisture sensors , NDVI

sensors and data loggers that integrate with ZENTRA Cloud : an easy-to-use web software that seamlessly integrates into third-party applications through an API. Kevin Hyde, Montana State Mesonet Coordinator, says the system enables better spatial distribution and reliability. “When we were deciding on equipment we asked ourselves: What kind of technology should we use? It had to provide high data integrity. It had to be easy to deploy and maintain. And it had to be cost effective. There’s not a lot of people in that sector. METER systems are low profile, they’re affordable, and the reliability is there. I look at some other mesonets, and they cannot afford to build out further because they are relying on large, complex, expensive systems. That’s where the METER system comes into play.”

Read full Montana Mesonet case study—>

Weather station questions you should ask yourself

To sum up, your measurement needs and application define the required performance and value of a particular weather station. Important questions to ask before you buy are:

• Does this weather monitoring system have to be ultra robust?
• Does this system have to be hyper accurate and stable?
• Will the station be at a field plot that my technician can visit and maintain once a week, or is this a site I’ll only be able to visit once every two years?
• What are the particular measurements I want?
• Does the weather station have three season vs. four season capabilities?
• What are the power requirements? Can it run indefinitely on small batteries?

If you think through the various factors that drive the performance relative to your measurement needs, then it becomes a lot easier to decide what’s important, and you can find the best value.

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Weather station FAQs

Which weather station is best for greenhouse applications.

A greenhouse is an interesting environment to measure in. Some all-in-one stations are well suited for the greenhouse. The trick is to find a measurement suite that doesn’t include precipitation, so you’re not paying extra for that measurement. Part of the challenge in the greenhouse is artificial lighting. If you’re trying to measure photosynthetically active radiation then you’ll need to pay attention to your quantum sensor because most of the greenhouses are going to LED lighting which emit in discrete bands. If your quantum sensor doesn’t measure in that band, then you’re going to get the wrong answer. But there are plenty of options if you’re just looking for temperature and humidity. Even wind is important sometimes. But I would suggest one of the all-in-one weather stations for that application.

What would be the most and least frequent data logging for conventional meteorological parameters for soil plant atmosphere interaction?

This is a complex question because most of the power expense for weather monitoring systems is in broadcasting the data back to the cloud. If you need near-real-time observation, most weather stations can record every 5 to 15 minutes. But most people doing a field study in the soil-plant-atmosphere continuum collect data every 30-60 minutes. You can program your logger and some weather stations are pre programmed to give you maximum and minimum gust wind speeds so you don’t have to over sample.This will prevent you from having to do the post processing for terabytes of data.

What type of weather monitoring system would you recommend for highly granular monitoring such as hillside grapevines?

The all-in-one stations are tailor made for this application. You can find all-in- one stations in the $2,000 dollar range that will make accurate measurements. You can put several of those in the vineyard depending on topography to understand the spatial differences that are going to drive your irrigation decisions in that vineyard.

How do you test weather station sensor performance and the need for sensor recalibration?

You can buy a tier-1 sensor, like a tier-1 pyranometer weather sensor that has traceability back to Davos, and compare your solar radiation measurements against that standard. You can also buy a well-calibrated platinum resistance thermometer with an aspirated radiation shield to measure air temperature. Then you would compare your air temperature measurements against that. If you make these studies over the long term, you can quantify the drift in a particular sensor and come up with some reasonable recommendations for recalibration. At METER, we spent a lot of time doing that with our ATMOS 41 weather station. We quantify the drift we’ve observed and come up with recommendations for recalibration or refurbishment that make sense.

How can you account for dealing with maintenance costs and support from various companies producing these weather stations?

Maintenance costs can be significant. Sending people out to maintain weather stations in large networks is expensive. Most of the manufacturers of weather stations are going to give good support. So if you have a problem, you’re going to be able to find the answer. However, if you attend the American Meteorological Society meetings and look at new instrumentation, there are a number of new companies with offerings that look similar to instrumentation from reputable companies with a long track record. These new stations may not have the same performance. So you need to make sure to buy from a reputable company.

How important is metadata for the representativity of the data series and model calculations?

Knowing when your various sensors were produced and calibrated, plus understanding sensor height and location is extremely important. WMO recommendations ensure that you have the right supporting metadata, but even in research applications, metadata can make or break a study. You may have a colleague leave and all of a sudden their lab notebook has disappeared. You’ve got data coming in, but you don’t know what those data mean because you’ve lost all the supporting information. At METER we’ve spent a lot of time making metadata available all the way from the sensor through the ZL6 data logger and into ZENTRA cloud. So you have those metadata in all of your permanent records

What have you seen performance wise with the ATMOS 41 during winter, extreme winter conditions?

The ATMOS 41 all-in-one weather station is a three-season instrument, so it is not heated. In winter, the main drawback is that your funnel will fill with snow and you won’t get any precipitation measurements during the frozen part of the year. There is also the possibility that snow and ice could pack the sonic anemometer opening and attenuate wind speeds as well as the air temperature. A good wintertime precipitation measurement is a pretty intensive process. It typically takes a heated weighing gauge that you put a little oil and antifreeze on top to make sure that it doesn’t freeze and or evaporate. So it’s power intensive and pretty difficult to do right.

Does the ATMOS 41 and ZL6 conform to the ASABE Automatic Agricultural Weather Stations Guidelines?

From Table 1 of “ Measurement and Reporting Practices for Automatic Agricultural Weather Stations “, the ATMOS 41 internal measurement sequence meets the sampling interval guidelines for the weather variables listed. The ZL6 data logger can be configured to report values each hour, as stated in Table 1; however, some min/max instantaneous values are not available when using the ZL6 for data acquisition and delivery. Consult the ATMOS 41 user manual for details on the output values processed in METER data loggers.

What is the footprint of the ATMOS 41?

The ATMOS 41 is a microclimate sensor, so you should position it to be representative of the climate relevant to the research questions you are asking. FAO56 gives guidelines to the positioning and field size of sensors, so if you intend to use the sensor for reference ET , follow those guidelines. The footprint of a micro-meteorological measurement depends on the height of the sensor(s), the wind speed, and the sensible heat flux and is not a simple calculation.

How frequently does the ATMOS 41 all-in-one weather station make measurements?

The ZL6 makes a measurement from each of the sensor ports in use every 60 s. However, the minimum measurement interval is five minutes for uploading data to ZENTRA Cloud. One-minute measurement interval is possible if you disable uploading data to ZENTRA Cloud, and these instructions are available upon request.

The ATMOS 41 measures the solar radiation and temperature once every 10 s and records the instantaneous values. When queried, the ATMOS 41 outputs the average of the instantaneous measurements since the last query.

The ATMOS 41 measures the wind speed and direction once every 10 s and records the instantaneous wind vector components. When queried, the ATMOS 41 outputs the average of the instantaneous measurements since the last query for wind speed and direction and the maximum instantaneous wind speed value for wind gust.

If using a non-METER logger, then the ATMOS 41 can be scanned every three seconds, but it is not necessary to oversample the ATMOS 41 and compute averages, accumulations, and maximums in external data systems because the ATMOS 41 has an internal measurement sequence [see the integrator guide for more information]. Less frequent sampling has the additional benefit of decreasing data acquisition systems and ATMOS 41 power consumption.

Does the ATMOS 41 need to be powered continuously?

Yes. There isn’t a way to get meaningful data from the ATMOS 41 without powering it continuously and letting its internal measurement sequence operate. The ATMOS 41 could be powered up at a set interval, allowed to take the first set of measurements, and then those could be output. But this scheme would miss nearly all precipitation, nearly all lightning, and would grab a single, instantaneous value of wind speed and direction, which is almost meaningless considering the inherent variability in wind. One thing to note is that the ATMOS 41 has been designed specifically to use as little power as possible in normal continuous-power mode. The average current consumption is on the order of 200 micro-amps. Even if the non-METER data acquisition device runs on just a few AA cells, it should be able to sustain this power draw for a very long time.

How does the ATMOS 41 anemometer compare to other sonic anemometers?

See ATMOS 41 comparison testing and sensor-to-sensor variability data here .

What is the practical lower limit of the wind speed measurement for the ATMOS 41?

The practical lower limit for wind speed is about 0.03 m/s for our sonic anemometer. This is much better than cup anemometers, for example, which struggle making measurements below 0.5 m/s at the minimum because of difficulty starting and stopping. Sonic anemometers can read five times lower than that, yet they don’t necessarily read absolute zero.

How does the ATMOS 41 record an accurate air temperature without a radiation shield?

The ATMOS 41 collects all of the information necessary to correct for absorbed radiation in a biophysical model. Because the ATMOS 41 also measures wind speed and solar radiation, it is possible to use a simple energy balance calculation to correct the Tair measurement. After correction, error decreases to < 0.5 °C and yields better accuracy than commonly used passive ventilation radiation shields.

The equation and experimental results are available in our application note and in the video below.

What are installation best practices for weather stations?

1. Location: Make sure the location you choose for any weather monitoring system will give you answers to the questions that you want answered. If you’re looking for general weather monitoring, make sure the location is far (at least 3X height of tallest obstruction) from any obstructions to wind. Make sure the vegetation is representative, and make sure the topographical location is representative. Rooftops are pretty bad generally, as are deep valleys or hilltops. If you’re looking for reference ET, you’ll want to deploy in the field with at least a few meters of crop on all sides of the installation. Also make sure that nothing is going to shade the solar radiation sensor.

2. Height: A lot of groups mount the ATMOS 41 at 2 m height, because this is the norm for reference evapotranspiration. Others go higher for meteorological observations. Some even deploy in the canopy for specialized research questions. You can deploy easily at whatever height you want as long as you have the right mounting apparatus.

3. Mounting apparatus: The ATMOS 41 is designed to mount on a vertical rod (see the user manual and quick start guide for exact dimensions). It is often deployed on a vertical pole anchored by either guy wires or by a good-quality tripod. Some even mount on T-posts, preferably with some guy wires to add stability.

4. Level: This is important for the ATMOS 41. You need to have it level to within 2 degrees in both the X and Y. There’s a bubble level underneath the rain funnel that you can see from below and use to get level. The ATMOS 41 also outputs x and y level as standard outputs, so you can make sure you’re within 2 degrees of zero. You’ll need to use the guy wires to pull the mounting apparatus level or add some shims to achieve proper level.

5. Check the data flow before you leave the field: Take a laptop (or handheld device if you’re using the ZL6 data logger) and the right software to make sure all the connections are good and that your data acquisition system is recording and/or transmitting data properly. A good best practice is to set it all up in the lab or office first, troubleshoot any issues, and then go to the field.

6. Always take a complete toolset: You never really know what you’re going to need when troubleshooting unique situations.

7. Tidy up the wires: The single biggest failure mode for environmental sensors in a weather monitoring system is the wiring. Zip tying extra wire to the mounting mast can keep it from getting snagged by animals or whipped around in windstorms and unplugged from the data logger. Protecting the wiring in a cage or other container is great if you have the ability to do so. Any of these things make the installation look more professional, which is an added bonus.

8. For more info: Watch the webinar below—7 Weather Station Installation Mistakes to Avoid

Measurement insights

Scientific weather station performance data and weather sensor comparisons.

We performed comparison testing for ATMOS 41 vs. high-quality, research-grade non-METER sensors and conducted time-series testing for sensor-to-sensor variability. Here are the results.

Lab vs. field instruments—why you should use both

Lab and field instruments used together can provide researchers a symphony of information and can be used as powerful tools in understanding data and predicting a soil’s behavior over time.

Case studies, webinars, and articles you’ll love

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Webinar: Water potential 201: Getting the most from each measurement

There is more to learn from water potential

In this 30-minute webinar, research scientist and METER’s Director of Scientific Outreach, Leo Rivera, dives deeper into soil water potential, applications of the measurement, and how to make sure you are making the most of the tools available. Within this webinar he will discuss:

• Using water potential data to infer other processes and properties in soil
• The resources available to determine the right water potential ranges for your plants
• How to choose the right water potential sensor for particular applications
• The difference between soil water potential and plant water potential and when to measure each

July 11th 2024, 9am PDT

DIY Electronics Projects

Electronics Project Hub

IoT Based Weather Monitoring System Using Arduino

In this post we are going to construct an IoT based weather monitor system using Arduino which can report us weather status like atmospheric pressure, temperature, humidity, air quality, light intensity etc. of your locality in real time and the data from the sensors are logged to an IoT cloud service called Thingspeak for monitoring and analysis.

We will see:

• What is IoT based weather monitoring system?
• Why we need an IoT based weather monitoring system.
• Types of sensors involved in weather monitoring.
• Block diagram of IoT based weather monitoring system.
• Circuit diagram and description.
• How to setup your Thingspeak account?
• Program code for Arduino and ESP8266.
• How to upload data to generic ESP8266?
• Prototype images.
• How to operate the circuit?

What is an IoT based weather monitoring system?

It is a system that involves in acquiring weather and environment data using advanced electronic sensors and sending them to a web server via internet for real time weather monitoring and storage of data for future analysis and study.  

Why we need an IoT based weather monitoring system?

• Ease of monitoring your local weather conditions in real time from anywhere in the world.
• For storing weather and environment data for short and long term for studying weather pattern changes and to understand how human induced climate change affected your local weather.
• Easy deployment of the setup for monitoring local atmospheric conditions and microclimates for weather forecasting and prediction.

Types of sensors involved in weather monitoring:

We can find wide spectrum of electronic sensors involved in weather monitoring system depending on the kind of application.

For example:

Farmers need to know the temperature, relative humidity, soil moisture, rain fall etc. to enhance their crop production and the following type of sensors are utilized to obtain the data:

• Temperature sensor.
• Humidity / hygrometer sensor.
• Soil moisture sensor.
• Rain sensor etc.

For an airplane pilot he/she needs to know wind speed, wind direction, atmospheric pressure, precipitation, visibility etc. before they takeoff and they use the following sensors:

• Barometric sensor – for measuring atmospheric pressure.
• Anemometer – for measuring wind speed.
• Rain sensor.
• Visibility sensor – for measuring visibility during snow, rain, storm etc.

In conclusion, the sensors used for monitoring weather depends on the type application we going to deploy for.

Block diagram of IoT based weather monitoring system:

We are going to develop the weather monitoring system using the above illustrated blocks. The brain of the project is an Arduino board and the surrounding blocks are digital and analog sensors for acquiring local weather and environment data.

A generic ESP8266 is used for interfacing the circuit setup with internet via 2.4 GHz Wi-Fi band. The ESP8266 sends the sensor data to a cloud server where the data gets updated in real time and also gets stored for future analysis. We are utilizing a 16 x 2 LCD display to showcase the sensor data, so that we can observe real-time data locally.

Full circuit diagram for IoT based weather monitoring system:

The above circuit consists of the following modules:

• 5V / 3.3V Power supply regulator.
• Arduino Uno.
• 16 x 2 display with I2C adapter module.
• DHT11 temperature & humidity sensor.
• MQ-135 sensor air quality sensor.
• BMP180 – Barometric sensor.
• Light depend resistor (LDR).
• Generic ESP8266 Wi-Fi module.

Now, let’s explore what each of the module does and how it is interfaced with other modules.

Power supply 5V and 3.3V:

The above illustrated module takes 9V to 12V DC supply from a wall adapter and converts to 5V and 3.3V for providing power to Arduino board and sensors.

There are multiple 5V and 3.3V supply outputs in this module and you need to connect 5V supply to 5V sensor modules and 3.3V supply to 3.3V sensor modules. Mismatching will lead to malfunction of the respective modules / sensors.   

A button is provided on the module to turn ON/OFF the 5V / 3.3V output. A green LED indicates that the module is powered ON. There is also a USB power output but we won’t be using it for this project.

16 x 2 LCD display:

We are utilizing a 16 x 2 LCD display to showcase sensor data locally and it can display 16 alphanumeric characters in 2 rows.

An I2C display module is used in this project to reduce the number of wires that connect from microcontroller to LCD display to four; otherwise we need to connect 16 wires.

I2C display module operates on I2C bus and has the following four pins:

• SDA – Serial data.
• SCL – Serial clock.
• GND – ground.

The Vcc pin connects to 5V of the power supply module and GND connects to GND of the supply, the SDA connects to A4 of Arduino and SCL connects to A5.

The I2C module has backlight control, by removing the jumper we can turn off the backlight and vice-versa. You can adjust the display contrast by rotating the potentiometer (blue color on the module) using a small screw driver.

DHT11 temperature and humidity module:

DHT11 is a digital sensor responsible for collecting temperature and humidity data from your surroundings. It has three terminals namely:

Vcc connects to 5V supply, GND connects to GND and data pin connects to A0 of Arduino.

Note: The pin diagram for DHT11 module could differ from manufacture to manufacture and it is recommended to take a good look at your module to see which pins are Vcc, GND and Data before you apply power to the circuit.

BMP180 barometric sensor:

The above illustrated module is a barometric sensor which is capable of measuring atmospheric data; it can give out data like, atmospheric pressure at ground level, atmospheric pressure at sea level and altitude.

We will be only extracting atmospheric pressure data at ground and sea level to display it on the LCD, but we will be sending only the atmospheric pressure data at ground level to Thingspeak server, which is the relevant data for your locality.

It has the following pins:

• Vcc – 3.3V.

Care must be taken while connecting the supply to this module as it operates on 3.3V DC and 5V will kill the module. It operates on I2C bus, same as I2C display adapter module.

MQ-135 air quality sensor module:

MQ-135 is an analog air quality sensor which takes air samples from your surroundings and gives out an analog voltage at its output terminal. MQ-135 can detect the following gases:

• Smoke, CO2 etc.

The operating voltage of MQ-135 is 5V and consumes around 160mA, the sensor has built-in heater for heating the sensor for its normal operation and if the sensor is exposed to strong wind we may get incorrect readings. The sensor takes typically around 3 to 5 minutes to reach optimum temperature depending on surrounding air flow.

The sensor has good sensitivity to detect the above mentioned gases, but the disadvantage is it cannot differentiate which gas or gases have been detected.

Generic ESP8266 module:

The above illustrated module is called generic ESP8266 which is responsible for connecting the weather monitoring system to internet. This module is inserted on a breakout board adapter so that ESP8266 can be interfaced on a breadboard.

Pin diagram of ESP8266:

ESP8266 is not a just another ordinary module, it has a full-fledged 32-bit microcontroller which requires a program code to function. We will be using a programmer to upload the code to this ESP8266 module which we will see in the later part of this article. It operates on 3.3V and communicates on serial interface with Arduino.

Light depend resistor – LDR:

LDR is responsible for collecting data about the intensity of light at your surroundings and it is a passive analog sensor.

The LDR is essentially a resistor that is sensitive to the light, when higher intensity light falls on the photosensitive surface its resistance drops and when less light is received its resistance increases.

In other words, the resistance is inversely proportional to the intensity of the light on the photosensitive surface of LDR. 

This concludes all the modules and sensors used in the circuit setup.

Program code for Arduino:              

I2C LCD library : Click here

DHT library : Click here

BMP180 library : Click here

Program code for Generic ESP8266

Download Thingspeak Library : Click here

Insert your Wi-Fi credentials here the code:

Insert your Thingspeak credentials here:

How to upload code to generic ESP8266 module:

• You need to download ESP8266 board package.
• You need an ESP8266 programmer board.

1) How to download ESP8266 board package?

• Copy this link:  http://arduino.esp8266.com/stable/package_esp8266com_index.json
• Now open Arduino IDE and click on  File > Preferences .
• A window will open like this:

• Paste the URL on the box and click “OK”.
• Now go to  Tools > Board > Boards Manager.

 A window will popup:

• Type “ESP8266” on the box as shown and you will get installation option, select the  latest version  and click install.
• Now the IDE will download the necessary packages and this could take more than 5 minute to complete.
• Now go to  Tools > Board > ESP8266 boards > select “Generic ESP8266”.
• Now, copy the given ESP8266 program code and paste it on to Arduino IDE software.
• Now press compile button (Green tick button). The compilation of code may take more than couple of minutes so be patient. If the compilation failed please check whether have you selected the “Generic ESP8266 Module” in the board option or not.
• After successful compilation of code, now it’s time to upload the code to ESP8266.

2) USB ESP8266 Programmer:

The above illustrated USB device is used for programming the generic ESP8266 module.

Please note that two pins on the programmer must be shorted before you upload the code, otherwise you will get errors while uploading.

Now insert the ESP8266 on the programmer and plug it to your PC’s USB port, like this and click upload:

Once you successfully uploaded the code, you will the below info on your IDE:

Now, you may insert the ESP8266 on your main circuit setup.

How to setup your Thingspeak account for receiving data?

• To send sensor data to Thingspeak, you need a Thingspeak account and you can sign-up here .
• Create a new channel and do the following to your Thingspeak channel:

• Scroll down and press save.
• Please take note of your channel ID and you need to copy and insert the ID to the ESP8266 code.
• Now go to API key tab and you will see “write API” and “read API” keys. Write API key is a secret code for writing data to your Thingspeak channel.

• You need copy and paste the write API key to the ESP8266 code.

Prototype images of IoT based weather monitoring system:

How to operate the IoT based weather monitoring system:

• Make sure that you have connected all the wirings properly and all the modules are connected.
• Plug a 9V to 12V DC adapter to the power supply module’s DC socket and press the ON switch.
• You will see sensor data on the LCD cycling between sensors as shown below:

DHT11 temperature and humidity:

BMP180 atmospheric pressure at ground and sea level (unit is Pa):

MQ-135 Air quality sensor:

LDR sensor – intensity of the light:

Lower percentage indicates low light intensity and higher the percentage indicates higher light intensity.

Please note that air quality and light intensity values are between 0 and 100% and no unit. Also please note that MQ-135 sensor needs to heat up for its proper functioning, so initially while the sensor is heating up it will throw incorrect values and incorrect quality status like toxic or poor.

Data on Thingspeak:

• DHT11 sensor:

BMP180 and LDR:

MQ-135 sensor:

Note: Initially zero value will get updated on all the 5 fields on Thingspeak and after that real time data starts updating. 

If you have any questions regarding this project, feel free to ask us in the comment, you will get a guaranteed reply from us.

My nick name is blogthor , I am a professional electronics engineer specialized in Embedded System. I am a experienced programmer and electronics hardware developer. I am the founder of this website , I am also a hobbyist, DIYer and a constant learner. I love to solve your technical queries via comment section.

81 thoughts on “ IoT Based Weather Monitoring System Using Arduino ”

I want the email please sir to communicate with you regarding this project is necessary.

Yes, you can ask you questions here…

Do we need usb cable for data to be transferred to thingspeak , or will it be updated through wifi

Hi, it will be updated through Wi-Fi.

Hlo sir, I am getting error while i try to upload the code.

Hi, Please add all the libraries as mentioned in the post

value_1 = ((value[1] – 0x30) * 10 + (value[2] – 0x30)); value_2 = ((value[3] – 0x30) * 10 + (value[4] – 0x30)); value_3 = ((value[5] – 0x30) * 10000 + (value[6] – 0x30) * 1000 + (value[7] – 0x30) * 100 + (value[8] – 0x30) * 10 + (value[9] – 0x30)); value_4 = ((value[10] – 0x30) * 10 + (value[11] – 0x30)); value_5 = ((value[12] – 0x30) * 10 + (value[13] – 0x30));

Can u please explain the above code?

Hi, The input data from arduino to ESP8266 is received as stream of individual number, here we are grouping the numbers of a sensor using array, so that a sensor data is combined correctly before sending it via internet. For Example, LDR data is sent to ESP8266 as 1 0 0 for 100%. The above block of code combines individual digits to 100 before sending to cloud. The received individual digits by ESP8266 will be their ASCII values, so to convert them to proper integer (before combining) we are subtracting the received individual data with 0x30 hex value. Regards

Thanks a lot:-) Can u please say which value corresponds to which sensor… And how is array length 15?

Hi, Value 1 & 2 are DHT11, Value 3 is BMP180, Value 4 is LDR, Value 5 is MQ-135. Regards

Hello, Could anyone please tell me what is the programming language used for programming here? Thankyou

C language.

Thankyou so much for you’re previous reply. I had another doubt like how the power supply given to the entire circuit?

Hi, The circuit requires 3.3V and 5V, in our circuit setup we used a power supply that accepts 9V in and 3.3V & 5V out. Regards

I am getting DHT 11 SENSOR ERROR others are working what may be the issue. Sometimes it shows the temperature and Humidity but majority of the time it shows as DHT 11 SENSOR ERROR

Hi, There must be loose connections on one or more wires of the sensor.

I am using Nodemcu instead of ESP8266 is there any changes in the code I need to make. Please Share. I am getting output in LCD the problem is I am not getting serial output from Arduino and Node MCU as well as thingspeak. Please help.

Hi, We need to make the project from starch if we want to use NodeMCU.

We got the method and all other things about this project but i have to make it with model so can u suggest some example for what mode can i make for iot based weather monitor ???

Hi, You can use any plastic junk box as enclosure for the project and make some vent hole so that there will be proper air circulation for the sensors. Regards

Please check your VCC and GND connections. Check DHT11 sensor out cable connection.

Can we add gsm module in this project.

Hi, Yes you can, also we need to do some additional programming depending your requirements.

Hi. Thanks for the code and setup. But when uploading my code to esp8266 board, I am recieving following error message: esptool.FatalError: Failed to connect to ESP8266: Timed out waiting for packet header

Can you please tell me its meaning and how to solve it?

Hi, Are you using the illustrated programmer kit? If so you need to short the two terminals as shown in one of the images. Usually, it takes only one attempt to program the ESP8266. Regards

What are the advantages over other projects of the same theme?

Hi, This circuit is well tested one and we have integrated a lot of relevant sensors. Regards

Thingspeak shows only zero values

Initially it will show zeros just for one cycle then, it will update actual values. If you can see values on your LCD check Tx and Rx connections between Arduino and ESP8266 for Tx and Rx. Don’t alter any delay in the code.

If arduino nano is used, does the programming code change?

Yes, you can use arduino nano.

1.Sir, after how much time it will show the data, meaning after how long we can see the weather.

2. Sir how we check the practical data with actual data.

Hi, The data will appear after 1 to 2 mins on Thingspeak, initially it will update zeros and then sensor data starts updating. I did not understand your 2nd question. Regards

Hey buddy it’s showing dht11 sensor error

Hi, Please find its correct Vcc, GND and output Pin. These are differs from manufacture to manufacture.

Sir, can we see the results in mobile?

Hi, Yes, you can by logging into your Thingspeak account on your browser.

Is there any alternate method to upload code for wifi module.

Yes, there is check this article and scroll down to the section where I explained how to upload it.

MQ 135 is showing constant data and it is not varying while connecting to arduino what is the i am not understanding

can you please tell what is the constant value? It is a wiring issue on your setup.

I am able to get the output on the serial monitor in the Arduino IDE but unable to get the output on the things speak app.i have uploaded the code on the esp8266 also but unable to get on the things speak app.please help me

Assuming you have correctly put your SSID, password and write API key on ESP8266 code, then the only thing is to check your wiring and more importantly check Vcc and GND voltage at ESP8266’s terminal, it should be more than 3.0V only then it can upload the data.

It is showing 16 and good throughout the run time

Hi, it is a wiring fault please double check your wiring connection.

I have uploaded the esp8266 code in the esp8266 and it successfully uploaded .I have connected the circuit as mentioned above but unable to get the data on the things speak app.i have uploaded the correct wifi credentials and thingspeak credentials.But unable to get the data on the things speak app.It is also not showing zero also.i am unable to solve the issue .please can you tell me whether I have to do any extra things.please help me

Check the voltage at Vcc and GND terminal of ESP8266 it should be above 3.0V and lower than 3.3V.

sir i need your help with this project my name is kiran and i choose this project as a mini project in my college so disclosing your contact info here is unsafe so iam going to post my mail id here so that you can contact me [PROTECTED]

Hi, You can ask you queries here and I will try to address it here.

Sir, this might be a dumb question but can you use a powerbank to power the board aka the power regulator?

You you can, but still you need to provide 3.3V from a regulator.

I see, can you explain the part of uploading the code to arduino cuz I got confused between uploading the esp8266 code first and arduino code later? Thanks for the reply btw 🙂

Hi, You need to remove the microcontroller from the arduino board so that ESP8266 can be programmed, later you can reinstall the microcontroller to the board and upload the code designated to arduino. You can also upload the code for arduino first, but to make things easy for newbies I mention that.

I see, thanks again for the help 🙂

Can I get the ppt of this project? Because it will use for my study to do my project.kindly send me sir.

Sorry, we did not document a PPT for this project.

I need help, already done uploading the code and everything but the lcd just flashing on and off without displaying anything and in thingspeak nothing showing up 🙁

You wiring to LCD is not proper and also check the voltage that is going to LCD.

Sir, I’m getting dht11 error on lcd and I didn’t get anything in thingspeak. I have checked my wiring and replaced them with new components but the results stays the same

Hi, Your issue with DT11 is pin configuration of the sensor, connect the sensor properly, its +Ve, GND and output. Even if any one of the wire is mismatched you will error. Regards

how data is analysed in thinkspeak ?

Hi, you can download the data from your Thingspeak account in various formats and you may analyze it visually or with a software.

What kind of simulation you using for the circuit?

No simulation was used for this project.

CAN YOU SEND THE CODE FOR STM 32 BECAUSE I’M doing it in STM 32 (BLUE PILL), SO PLEASE CAN YOU SEND IT WILL BE A GREAT HELP

Hi, We will try to publish one in future 🙂

I am using Nodemcu instead of ESP8266 is there any changes in the code I need to make. Please Share I need it

Yes, you need to make a lot of change to the code! You may need to develop from scratch.

How do I add rain sensor to it ?

Hi, you can add a rain sensor on any of the digital pins (if your rain sensor’s output is digital). Configure the pin as digital input, create a new function for the rain sensor, put your values to send_data() function. You need to understand the code before you can start changes in your project. Regards

The ESP8266 is sending all values as 0. I have checked the RX and TX wires. They are connected properly.

Any modification from your side done in the code? only a few time it should update zero at max after that it should update the values.

i got dht 11 sensor error, my all wiring is totally ok, please help about the error

Hi, either you sensor is damaged or still your wiring to DHT11 is incorrect.

I am getting nan at the output terminal:

Hi, The serial terminal was not configured in the first place in this project 🙂

Sir the data is not showing in thingspeak website

Hi, Please give us more info about your circuit setup, is it updating on LCD display etc.

Hi sir I am facing an issue with the uploading of the arduino code it always says “programmer is not responding” what to do now ??

Are you using the programmer module?

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Iot Based Weather Monitoring System Using Nodemcu and Blynk

Kilby 100: 7th International joint conference on computing sciences

6 Pages Posted: 11 Apr 2024

Gigyashu Kumar

Lovely Professional University

Ankit kumar

Jamal akhtar khan.

Date Written: May 5, 2023

Systems for monitoring the weather are crucial instruments for collecting and analyzing information on diverse atmospheric conditions. The capacity to give real-time data on weather conditions has made Internet of Things (IoT) based weather monitoring systems more and more common. This study introduces a Nodemcu and Blynk-based IoT-based weather monitoring system. A Nodemcu microcontroller, sensors for measuring temperature, humidity, and air pressure, as well as a Wi-Fi module for connecting to the Blynk platform, are all part of the system. With the help of the mobile app platform Blynk, customers can access and manage their IoT devices from anywhere in the world. The system is made to gather weather-related data, send that data to the Blynk platform, and deliver real-time weather updates via a mobile app. The Nodemcu microcontroller, which connects to the sensors through a breadboard and communicates with the Blynk platform via the Wi-Fi module, makes setting up the system simple. The Blynk app allows users to set alerts depending on various weather conditions and shows the data received by the sensors in real time. A wide range of industries, including agricultural, aviation, marine transportation, and crisis management, can benefit from the proposed IoT-based weather monitoring system. It can be used in agriculture to track soil temperature and moisture levels as well as to forecast weather for crop planning. It can give pilots current weather information for secure takeoffs and landings in aviation.It can offer information about sea conditions for navigation and safety in marine applications. It can be used in disaster management to track and predict weather patterns and to issue early warnings for extreme weather occurrences. Overall, this paper introduces a Nodemcu and Blynk-based IoT-based weather monitoring system that is simple to set up and offers real-time weather data. The technology can be utilized for a variety of purposes, including enhancing our capacity to comprehend and react to weather-related phenomena.

Keywords: IoT, weather monitoring, data logger, Nodemcu, Blynk, real-time data, temperature, humidity, atmospheric pressure, agriculture, aviation, marine, and disaster management.

Suggested Citation: Suggested Citation

Gigyashu Kumar (Contact Author)

Lovely professional university ( email ).

Jalandhar, Punjab India

Ankit Kumar

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IoT Based Weather Monitoring System For Micro-Climate Forecasting

Jan 1, 2022 | Blog , IoT Based Weather Monitoring System

Forecasting the weather has come a long way from where it once used to be. Not too long ago, the only way to find out about the day’s weather was through local news channels, radio stations or through aviation weather services. Now, it’s just one click away—all you have to do is check your smartphone.

Environmental monitoring has become incredibly important, given people’s need to be aware of the latest weather conditions wherever they go but more importantly, whatever their profession. Technology is evolving at a dynamic rate, and weather stations need to adapt in order to keep pace with consumer and commercial demand for accuracy. And one of the most important factors that must be considered are microclimates.

What are microclimates?

Weather is not always forecast accurately. One of the biggest drawbacks of the current weather forecasting system is that it is done on a grid. To produce day-to-day forecasts, global model grid lengths range between 9 and 13 km in the smallest sections of the grid all the way up to hundreds of kilometers, leading to inaccurate and inconsistent reports.

These grid systems can not consider microclimates , which are much smaller pockets of land with distinct features that affect temperature, precipitation, and wind.

Some common microclimates include:

• High-elevation areas
• Coastal regions
• Large forests
• Urban landscapes

To improve weather forecasts in a very specific location, it becomes important to understand and identify microclimate conditions. Whether it’s the topography or construction elements affecting different weather factors, accurately collecting data is essential for microclimates around the world.

So how does a microclimate form? It depends on different topographical features that would affect normal climate and weather patterns. For example, the contrast in topographies can affect airflow, ambient temperature, and cloud formation. The particular soil composition of land can have an effect on its rate of evaporation. Heavy vegetation on a specific area can affect airflow as well as moisture levels; this can be natural or manmade.

Microclimates aren’t limited to differences in nature; unnatural additions to a particular landscape can also create them. Urban centers generate high amounts of heat and smog, which can affect low- and high-level winds. Several acres of parkland in the middle of a city can cause multiple microclimates to emerge in a very small geographic area as well. The same principles apply to agriculture as well; differences in crops, irrigation practices, etc. can affect the microclimate as well.

Due to the very specific characteristics of locations with microclimates, it becomes difficult for a traditional grid-based monitoring system to consider different environmental factors. For example, certain variances can have a significant impact on predicting the path of a hurricane or a snowstorm. For someone who lives in or near such microclimates, the forecast as seen on TV may not be accurate.

In addition, conventional grid-based weather monitoring systems are bulky, expensive, and require constant surveillance . Data is transferred manually, which increases the margin for error and runs up expenses.

Here’s where an IoT-based weather monitoring system steps in.

What is the Internet of Things (IoT)?

So what exactly is the “Internet of Things” (IoT)? Simply put, it is a technology that looks to connect all kinds of devices and sensors to share the data obtained from various locations. Today, IoT devices are used across all industries, processing and analyzing data to coordinate traffic signaling, ensure industrial safety, monitor medical applications, or even regulate the temperature inside a house.

The Internet of Things offers a wide range of connected devices with different protocols in order to achieve complete machine-to-machine interaction. It is viewed as one of the biggest innovations in the data industry. This system connects everything to the internet, with the aim of exchanging data to trigger corresponding actions when needed.

IoT envisions a near-future where regular day-to-day objects will be fitted with microcontrollers and convention stacks that will make the devices ready to talk to each other. It looks to recognize, find, follow, observe, and oversee data across many different gadgets. The end goal of IoT is to make the internet more immersive and helpful.

The technology is an advanced solution for connecting different devices to the internet (in this case, the factors affecting the micro-climate of a region) and connecting them within a network. With the use of sensors and automotive electronic equipment, an advanced system can be developed to keep track of the many different characteristics of the climate, such as temperature, humidity, wind speed and direction, rainfall, and more. The system can then analyze all collated data and generate accurate microclimate forecasts that can be displayed in real-time on a digital screen ( this is part of what we are doing for agriculture here at Benchmark Labs ).

An IoT-based weather monitoring system connected to data centers could also keep track of past information, either on an hourly or a daily basis. The sensor data can be analyzed and plotted in graphs and statistics to further improve forecasting.

What does an IoT-based weather monitoring system entail?

IoT-based weather monitoring technology has been growing very quickly, and it has already bypassed the features and functionalities of conventional systems in many ways. Having been applied in the field of remote monitoring and advanced analytics, the technology is revolutionizing different ventures while offering a range of benefits.

Innovations in weather monitoring have focused on controlling and monitoring different weather conditions. IoT devices help measure the physical parameters of a certain location and upload them in real-time to cloud storage, where the data can be analyzed immediately.

These systems make climate monitoring in difficult geographical terrains possible. The manpower required for accurate microclimate forecasts is significantly reduced, and there is no longer any need to physically go into inhospitable environments just to get accurate measurements. Sensor devices placed in particular locations can do all the work to detect current climate details, such as rainfall, wind speed, humidity, soil moisture, CO2 levels, and other data needed for forecasting.

Going a step further, when one connects the weather station to the internet, IoT can be used much more extensively in predicting and disseminating accurate weather data in a particular location. This information can then be made available anywhere in the world.

Wireless weather monitoring significantly impacts businesses across different industries. Data received from sensors can be collected by microcontrollers and dispatched where it is needed. When leveraging IoT technology, other systems such as home automation, wireless sensor networks, and control systems can work smarter and more efficiently.

Accurate and timely microclimate forecasting using IoT plays a very important role in the field of agriculture, as it can provide farmers and landowners specific weather information that will dictate their daily operations. This technology can also make it possible to safely monitor extreme weather conditions in inhospitable environments.

Here are some of the advantages of using an IoT-based weather monitoring system when it comes to microclimate forecasting:

• The process is fully automated
• Does not require human attention
• Prior alerts of accurate weather conditions
• Low cost and efforts in the system
• High accuracy
• Self-protection
• A smart way to monitor the environment
• Efficient & time-saving

What’s next?

Understanding the conditions in a microclimate can help meteorologists and locals alike receive an accurate and specific weather forecast. Given the many factors that affect temperature, precipitation, and wind, accurate data collection and real-time analysis are more critical than ever.

IoT combined with the traditional grid forecast provides an efficient and low-cost solution for continuous monitoring of the environment. These sensor devices and robust systems can easily collect data and create a more accurate picture of a microclimate.

If you would like to learn more about Benchmark Labs from our team and sign up for a trial, go to our sign-up page .

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Source tracing of pm2.5 in a metropolitan area using a low-cost air quality monitoring network: case study of denver, colorado, usa.

1. Introduction

2.1. pm 2.5 monitoring network, 2.2. wind fields, 2.3. statistical and clustering analyses, 2.4. back-trajectory modeling, 3.1. descriptive analytics, 3.2. source tracing by statistical analyses, 3.3. source tracing by back trajectories, 4. limitations, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Afshar-Mohajer, N.; Shaban, M. Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA. Atmosphere 2024 , 15 , 797. https://doi.org/10.3390/atmos15070797

Afshar-Mohajer N, Shaban M. Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA. Atmosphere . 2024; 15(7):797. https://doi.org/10.3390/atmos15070797

Afshar-Mohajer, Nima, and Mirella Shaban. 2024. "Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA" Atmosphere 15, no. 7: 797. https://doi.org/10.3390/atmos15070797

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Remedial Measures for a Rainfall-Induced Creeping Landslide: A Case Study

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• Xuan Kang 3 , 4 ,
• Shun Wang 4 ,
• Wei Wu 4 &
• Guangli Xu 3  

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This paper presents a case study on the remedial measures of a rainfall-induced creeping landslide near the coastal area of China. The relationship between landslide activity and hydrological conditions is investigated through an integrated monitoring system. In-situ monitoring results indicate that the creeping motion of the landslide is mainly regulated by seasonal rainfall. Considering the on-going activity of the landslide, some remedial measures, including slope cut, superficial and underground drainage, were carried out for mitigating the potential damage to the railway transportation at the toe of the landslide. The subsequent monitoring result reveals that all the remedial measures effectively relieve the activities of the landslide.

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Institut für Geotechnik, Universität für Bodenkultur Wien, Feistmantelstraße 4, 1180, Vienna, Austria

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Kang, X., Wang, S., Wu, W., Xu, G. (2024). Remedial Measures for a Rainfall-Induced Creeping Landslide: A Case Study. In: Rotaru, A. (eds) Knowledge Transfer in the Sustainable Rehabilitation and Risk Management of the Built Environment. KNOW-RE-BUILT 2021. Springer Series in Geomechanics and Geoengineering. Springer, Cham. https://doi.org/10.1007/978-3-031-43455-6_5

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