Category Archives: Meteorology

RMS integration with Met Office DataPoint. An experiment with APIs…

The latest Rotronic Monitoring System software has been designed specifically for the IoT and IIoT world. We have a wide and growing range of sensors, loggers as well as input and output modules but we will never keep up with the unique demands of our customers. That’s where integration is key for any successful continuous monitoring system!

iot

Hardware can already be integrated via analogue input modules such as our 8ADC and digital devices can be integrated via our RMS-Convertor that can be programmed with custom protocols and functions operate with virtually any device.

Want to cut out the waffle… login and see the live data now using the details below:

https://rms.rotronic.com/rms/
Company Name: Rotronic demo-cloud
User: Weather
Password: guest1234

In addition to hardware, software integration is a must, and not easy when we consider RMS is a fully Gamp6 compliant system and therefore security and traceability is key.

Why not access the SQL DB?

All data on RMS is stored within an SQL database which with suitable rights can be queried easily to pull data out. However injecting data whilst possible triggers our system to report data manipulation. Also direct access to the database presents a security risk and uncontrolled changes to the system, and of course its not possible on shared systems.

That is why we also offer a Restful API through which data can be posted only when configured by users with appropriate permissions and each data stream is securely linked to a onetime token, by no means the best security but suitable for many applications (and of course the whole API function can be disabled if preferred). We of course have software wizards at our HQ that can develop professional integration solutions but as a hobbyist I wanted to see what I could achieve.

 

 

Example API Report

So my plan was to use Python and pull data from the Met Office DataPoint service and inject it directly into our RMS server software so it could visualised, reported and analysed accordingly. Just a few simple steps…

  • Step 1 Get the data from Met Office API
  • Step 2 Create API device in RMS and send your data
  • Step 3 Enjoy graphs, reports and custom alarms

Step 1 – Get the data from Met Office API.

The Met Office API is great you simply need to register to get an api key then get your head around the commands. Once you have that you can request the data you need via a simple url and the information is returned in xml or json format.

API Example
Met Office Datapoint API Response in XML

In Python requesting the last 24 hours of hourly data from location 3212 (Keswick) looks something like this…

Import json, requests
url = ‘http://datapoint.metoffice.gov.uk/public/data/val/wxobs/all/json/3212?res=hourly&key=YOURKEY’ #replace with your Met Office API key!
r = requests.get(url)
metoffice_data = json.loads(r.text)

This gives a Python dictionary with all the json data from which we can request specific values easily for example the latest conditions (no doubt there are more elegant solutions but this works for me).

Hum = (metoffice_data[‘SiteRep’][‘DV’][‘Location’][‘Period’][1][‘Rep’][-1][‘H’])
Temp = (metoffice_data[‘SiteRep’][‘DV’][‘Location’][‘Period’][1][‘Rep’][-1][‘T’])
Pres = (metoffice_data[‘SiteRep’][‘DV’][‘Location’][‘Period’][1][‘Rep’][-1][‘P’])
DewP = (metoffice_data[‘SiteRep’][‘DV’][‘Location’][‘Period’][1][‘Rep’][-1][‘Dp’])

Next we need to create our API device within RMS so it will accept our data

Step 2 – Create API device in RMS

Adding new API device in RMS is simple process, we create the device and define the Name and Serial number.

At this point RMS awaits an Post command in which the additional details are included. Using the Python code below I am able to create a device with 4 measurement points (measured values); Humidity, Temp; Pressure and Dew Point.

import json, requests

url = ‘http://rms.rotronic.com/rmsService/wService3.DeviceService.svc/UpdateDataJson’
headers = {‘Content-Type’ : ‘Application/json’, ‘Expect’ : ‘100-continue’, ‘Connnection’ : ‘Close’, ‘Host’ : ‘rms.rotronic.com’}

payload = {‘Name’:’API_Test’,’Serial’:’12345′,’Values’:[{‘Index’:’1′,’Typ’:’1′,’Value’:’50’},{‘Index’:’2′,’Typ’:’2′,’Value’:’23’},\
{‘Index’:’3′,’Typ’:’16’,’Value’:’5′},{‘Index’:’4′,’Typ’:’48’,’Value’:’1000′}]}
print (payload)
r = requests.post(url, headers=headers, data =json.dumps (payload))

Finally RMS gives us the device ID and API token which must be included in any future post commands.

Combining Step 1 and Step 2 allows us to simply replace my example values above with the real Met Office API data! Run the script hourly or permanently with an hour delay and we have a simple tool proving live data weather data!

Step 3 – Enjoy graphs, reports and custom alarms

With the data in RMS we can easily graph values and create email, sms or phone alarms. Taking the API further I it is possible download live satellite imagery and dynamically update the layouts in RMS!

Example Report

So it turns-out getting data into RMS via the API is simple with a bit of basic code. Of course Met Office data is just an example in modern industrial applications there is so much unique data from devices or software that might be of use and RMS aims to offer a complete monitoring solution not simply for our products!

Be sure to get in touch if you have any questions on the above or have any monitoring requirements. Use the demo login above or visit out RMS website for more details.

Dr Jeremy Wingate
Rotronic UK

 

Durable, Adaptable, Accurate

casestudy

Campbell Scientific (CSC) are an ISO 9001 certified company who are a leading manufacturer in a variety of applications related to weather, water, energy, gas flux and turbulence, infrastructure, and soil. Campbell Scientific, are committed to satisfying the instrumentation needs of their customers, and are internationally recognised in the measurement and control industry for producing accurate and dependable instruments

csc2

HC2A-S3

 

CSC systems are acclaimed for their dependability, which they demonstrate even in the most extreme weather climates. Their attributes include wide operating ranges, low energy usage, many communications options, and the flexibility to support a wide variety of measurement  and control applications. Applications include, agriculture, air quality, fire warning, water quality, weather and climate recording, structural monitoring, Geo-technical monitoring and mining.

Rotronic and CSC have been business partners for many years, CSC uses the standard Rotronic meteo probe in many applications. Recently CSC installed the probe in a network of Road Weather Information Systems  in Kelowna, British Columbia. CSC selected the probe because of its reliability, ease of use and accuracy. The HC2A-S3 is also highly regarded for its ability to function in extreme temperatures, this makes it good for the  Canadian climate, and a perfect complement to Campbell Scientific systems.

csc2
” We value the Rotronic HC2A-S3 probe for its ability to function at extreme temperatures.” Mike Ryder Campbell Scientific, Canada

For more information on the latest HC2A-S click here ,or for any of our products please visit the Rotronic website.

Meteorology Numerical Weather Prediction

casestudy

The calculation of weather data

What is the weather going to be like tomorrow?

For a long time, people have tried to predict weather conditions using the hydrologic climate cycle.In the early 1920’s scientists were able to compile a six hour forecast, back then it took six weeks to analyse weather data collected at only two points in Europe and calculate, by hand, a useful illustrative model. Today, supercomputers are used to predict the weather for a period of several weeks. The complex modelling programs require several million data points for parameters such as temperature, humidity, pressure, vertical & horizontal wind velocity with time stamps and absolute coordinates. To create a correlation between the data and the environment, scientists “slice” the atmosphere virtually into smaller horizontal &vertical parts—this process is called discretisation. It is more useful to compute the chronological change of the parameters using this model.

watercycle-pageClockwise from top left: Map of the average temperature over 30 years. .Weather station on Mount Vesuvius. .Water cycle summary

Meteorological events that are too “small” such as a single thunderhead, layer clouds or smaller turbulence’s will be parameterised through variables. This parameterisation is a science of its own that aims to reduce uncertainties as best as possible. Every forecast calculation starts with the current weather conditions. The quality of this input is crucial for the accuracy of the final forecast. Meteorologists link the forecast of yesterday’s weather with the actual measured parameters. Only large data centres are capable of computing this data assimilation. The overall result is a best possible calculation basis predict the weather for the next day. If this groundwork is flawed the forecast may be incorrect. For example, it could report rain at the wrong location. Today’s meteorological mathematicians also take parameters into account that change extremely slowly compared to the other factors. Growth and the reduction of polar ice, or the temperature of the oceans are summarised as boundary values. After a model is run using all the available data, meteorologists process and customise reports for a wide range of target groups such as public authorities, flight control centres, energy producers, industries and many more. These reports also include specific weather warnings.

Why the need to measure humidity?

Atmosphere_composition_diagramAtmosphere composition diagram

As described above, the daily weather forecast relies on the precise measurement of weather parameters. The science of numerical weather prediction aims to describe the daily hydrologic cycle in numbers. Humidity plays an  important role. Typically, data errors will multiply during calculations. Humidity values influence weather calculations e.g. through the water vapour balance equation— this formula expresses the influence of humidity through rain & condensation, and vice versa. Incorrect measurement or incomplete humidity data directly leads to wrong predictions of a huge number of weather phenomena such as the condensation altitude of clouds, locations of hyetal regions, fog layers and storms. In 1999, incorrect data sent by a weather station in Nova Scotia, Canada led to a incorrect forecast for Hurricane Lothar two days before it hit Central Europe. Authorities were insufficiently prepared to alert people in time. The prediction of rain and snowfall is still challenging for meteorologists. Only more extensive networks of weather stations and enhanced mathematical models will reduce problems due to unknown factors.

Facts & Figures

  • 7 inches is the diameter of the largest hailstone ever recorded.
  • Sukkur City in Pakistan is one of the most humid places in the world with
    30 °C dew point & a felt air temperature of 65 °C.
  • A study showed that a small thunderstorm system holds more than 10 million tons of water.
  • No two weather patterns are completely alike.
  • Some weather models assimilate data obtained from more than 25,000 weather stations.

Rotronic training course schedule 2016

We are pleased to announce our latest training course schedule for 2016. Courses include in partnership with Dave Ayres from Benrhos Ltd our practical 3 day temperature, humidity and dew point calibration and measurement uncertainty courses. In addition, for those seeking greater depth we are running dedicated courses on measurement uncertainty and ISO 17025 run by Lawrie Cronin and Dave Ayres

Temperature Humidity and Dew Point – Measurement, Calibration and Uncertainty

8th – 10th March :: 12th – 14th July :: 15th – 17th November
– Three day course at Rotronic UK offices and UKAS laboratory
– Practical applied knowledge and best practice
– Max 8 attendees to ensuring tailored content

Measurement Uncertainty for Laboratories and Plant

6th – 7th September
– Two day course at Rotronic UK offices
– Detailed knowledge for laboratory owners or process managers

Setting up and working with ISO17025

8th September
– One day course at Rotronic UK offices
– Ideal for ISO17025 lab managers or those looking to apply

For further information please do not hesitate to contact us.

 

Wind Turbines

Its been pretty windy recently, So wind farms are probably doing quite well at the moment. The biggest wind farm in the world, at the moment, is the London array, which can produce 630MW of power.

Wind Energy in General

The future is very encouraging for wind power. The technology is growing exponentially due to the current power crisis and the ongoing discussions about nuclear power plants. Wind turbines are becoming more efficient and are able to produce increased electricity capacity given the same factors.

Facts & figures:

There is over 200 GW (Giga Watts) of installed wind energy capacity in the world.

The Global Wind Energy Council (GWEC) has forecasted a global capacity of 2,300 GW by 2030. This will cover up to 22% of the global power consumption.

WindPower
Converting wind power into electrical power:

A wind turbine converts the kinetic energy of wind into rotational mechanical energy. This energy is directly converted, by a generator, into electrical energy. Large wind turbines typically have a generator installed on top of the tower. Commonly, there is also a gear box to adapt the speed. Various sensors for wind speed, humidity and temperature measurement are placed inside and outside to monitor the climate. A controller unit analyses the data and adjusts the yaw and pitch drives to the correct positions.

The formula for wind power density: 

W = d x A^2 x V^3 x C  

d: defines the density of the air. Typically it’s 1.225 Kg/m3. This is a value which can vary depending on air pressure, temperature and humidity.

A^2: defines the diameter of the turbine blades. This value is quite effective with its squared relationship. The larger a wind turbine is the more energy can be harnessed.

V^3: defines the velocity of the wind. The wind speed is the most effective value with its cubed relationship. In reality, the wind is never the same speed and a wind turbine is only efficient at certain wind speeds. Usually 10 mph (16 km/h) or greater is most effective. At high wind speed the wind turbine can break. The efficiency is therefore held to a constant of around 10 mph.

C: defines the constant which is normally 0.5 for metric values. This is actually a combination of two or more constants depending on the specific variables and the system of units that is used.

nordex-wind-turbine-450-x-299

Why the need to measure the local climate?

To forecast the power of the wind over a few hours or days is not an easy task.

Wind farms can extend over miles of land or offshore areas where the climate and the wind speed can vary substantially,
especially in hilly areas. Positioning towers only slightly to the left or right can make a significant difference because the wind velocity can be increased due to the topography. Therefore, wind mapping has to be performed in order to determine if a location is correct for the wind farm. Such wind maps are usually done with Doppler radars which are equipped with stationary temperature and humidity sensors. These sensors improve the overall accuracy.

Once wind mapping has been carried out over different seasons, wind turbine positions can be determined. Each turbine will be equipped with sensors for wind direction, speed, temperature and humidity. All of these parameters, the turbine characteristics plus the weather forecast, can be used to make a prediction of the power of the turbine using complex mathematics.

wind-turbine-controlThere is a small weather station on the top of this wind turbine

The final power value will be calculated in “watts” which will be supplied into power grids. Electricity for many houses or factories can be powered by this green energy.

Why the need to measure inside a wind turbine?

Wind farms are normally installed in areas with harsh environments where strong winds are common. Salty air, high humidity and condensation are daily issues for wind turbines.

Normal ventilation is not sufficient to ensure continuous operation. The inside climate has to be monitored and dehumidified by desiccant to protect the electrical components against short circuits and the machinery against corrosion.

Internal measurements are required to ensure continuous operation and reduce maintenance costs of a wind farm.

Philip Robinson                                                                                                       Rotronic UK

Meteorology: Numerical Weather Prediction

The Calculation of Weather Data

What is the weather going to be like tomorrow?

For a long time, people have tried to predict weather conditions using the hydrologic climate cycle.

In the early 1920`s scientists were able to compile a six-hour forecast. Back then it took six weeks to calculate by hand the weather data collected at two points in Europe and create a useful illustrative model.

Today, supercomputers are used to predict the weather for a period of several weeks. The complex modelling programs require several million data points for parameters such as temperature, humidity, pressure, vertical & horizontal wind velocity with time stamps and absolute coordinates. To create a correlation between the data and the environment, scientists “slice” the atmosphere virtually into smaller horizontal & vertical parts – this process is called discretization. It is more useful to compute the chronological change of the parameters using this model.

Meteorological events that are too “small” such as a single thunderhead, layer clouds or smaller turbulences will be parameterised through variables. This parameterisation is a science of its own that aims to reduce uncertainties as best as possible.

754334main_GOES-7Jun2013-0831EDT
Every forecast calculation starts with the current weather conditions. The quality of this input is crucial for the accuracy of the final forecast. Meteorologists link the forecast of yesterday’s weather with the actual measured parameters. Only large data centres are capable of computing this data assimilation. The overall result is a best possible calculation basis to predict the weather for the next day. If this groundwork is flawed the forecast may be incorrect, for example it could report rain at the wrong location.

Today’s meteorological mathematicians also take parameters into account that change extremely slowly compared to the other factors. Growth and the reduction of polar ice, or the temperature of the oceans are summarised as boundary values

After a model is run using all the available data, meteorologists’ process and customize reports for a wide range of target groups such as public authorities, flight control centres, energy producers, industries and many more, including the issue of specific warnings.

Facts & figures:

17.8 cm is the diameter of the largest hailstone ever recorded.

Sukkur City in Pakistan is one of the most humid places in the world with 30 °C dew point & a felt air temperature of 65 °C.

A study showed that a small thunderstorm system holds more than 10 million tons of water.

No two weather patterns are completely alike.

Some weather models assimilates data obtained from more than 25,000 weather stations.

Why The need to Measure Humidity?

As described above, the daily weather fore-cast relies on the precise measurement of weather parameters. The science of numerical weather prediction aims to describe the daily hydro-logic cycle in numbers – humidity plays an important role in this – data errors will multiply during calculations.

Humidity values influence weather calculations e.g. through the water vapor balance equation – this formula expresses the influence of humidity through rain & condensation, and vice-versa.

Incorrect measurement or incomplete humidity data directly leads to wrong predictions of a huge number of weather phenomena; this can include the condensation altitude of clouds, locations of hyetal regions, fog layers and storms.

In 1999, incorrect data sent by a weather station in Nova Scotia, Canada led to an incorrect forecast for Hurricane Lothar two days before it hit Central Europe. Authorities were insufficiently prepared to alert people in time.

hurricane-ivan_200_600x450
What is the Rotronic Solution?

Rotronic products are used in weather stations around the globe. They provide temperature & humidity data continuously with high accuracy even in demanding environments.

Rotronic manufactures a range of meteorological probes and weather shields to meet the standards required by meteorological organizations.

Philip Robinson

Rotronic UK