The rise of Meteo Monopolies: Consolidation within the Meteorological Industry

The meteorological sensor industry is in the midst of a hasty wave of consolidations by investment firms ever since global warming has become their latest fad. What does this mean for consumers? Consolidation at this unprecedented pace (see tables below), together with a hasty investment rush, results in a reduction in market competition. While market consolidation can be a good thing, the current pace of consolidation of established players, not startups, can be a sign of concern. In the long-term, reduction in market competition can only lead to lower quality overpriced products and strengthens barriers to entry for small independent players. Having experienced it first hand, it is the case today. Only large players have enough lobbying power to “guide” government tender specifications in their favor through corrupt practices in the developing world. One company, Varysian, is aiming to change that by opening up the conversation with meteorological departments’ leadership and technical personnel at the local level while adhering to the work of WMO, the Alliance for Hydromet Development and the wider UN Sustainable Development Goal 13 mandate.

The following tables list just some of the latest consolidations in the industry. 13 independent companies are now owned by 3. You be the judge, whether it is good or bad for the industry.

Six companies are now owned by one.
Davis Instruments	Jul 31, 2019	Davis Instruments acquired by AE Monitoring
LAMBRECHT meteo	Mar 31, 2019	LAMBRECHT meteo acquired by AE Monitoring
FTS Forest Technology Systems	Jan 2, 2019	FTS Forest Technology Systems acquired by AE Monitoring
Vieux & Associates	Jul 31, 2018	Vieux & Associates acquired by AE Monitoring
OneRain	Mar 8, 2018	OneRain acquired by AE Monitoring
High Sierra Electronics	Mar 8, 2018	High Sierra Electronics acquired by AE Monitoring

Three to one consolidation.
Nielsen-Kellerman	Oct 12, 2017	Nielsen-Kellerman acquired by Clearview Capital Investments for an undisclosed amount
Ambient Weather	Jan 2, 2020	Ambient Weather acquired by Nielsen-Kellerman for an undisclosed amount
RainWise	Jan, 2020	RainWise acquired by Nielsen-Kellerman for an undisclosed amount

Four to one consolidation.
Leosphere	Oct 5, 2018	Leosphere acquired by Vaisala
3TIER	Dec 17, 2013	3TIER acquired by Vaisala
Second Wind	Sep 15, 2013	Second Wind acquired by Vaisala
Veriteq Instruments	Mar 29, 2010	Veriteq Instruments acquired by Vaisala

Data for these tables was acquired from https://www.crunchbase.com.

Manufacturer of high-quality meteorological solutions for Smart-City environmental sensor networks including the MeteoHelix IoT, MeteoRain IoT and MeteoWind IoT wireless weather station and sensors.

Zu vermeidende Smart-City-Fehler: die Frage der großen gegen genaue Daten

DIE SMART-CITY-FEHLER DIE ZU VERMEINDEN SIND: DIE QUALITÄT VON DATENSÄTZEN MIT NIERIGER QUALITÄT WIRD NICHT VERBESSERT, WENN DIE DATENSÄTZE GRÖßER WERDEN

Wenn Sensornetzwerke nicht dem grundlegenden Messstandards entsprechen, werden Smart-City-Sensornetzwerke zu einem Geldfresser. Sie können großartige Ideen in eine sinnlose Infrastruktur und Wolken von falschen oder bedeutungslosen Daten verwandeln.

Zu Beginn des 21. Jahrhunderts begannen Städte im Rahmen der vierten industriellen Revolution (Industrie 4.0), mit Smart-City-Projekten zu experimentieren, noch bevor der Begriff Internet-of-Things (IoT) populär wurde. Jetzt, auf dem Höhepunkt des durch künstliche Intelligenz und Datenverarbeitung ausgelösten IoT-Hype, werden die ersten Anzeichen für die Notwendigkeit, die grundlegenden Messstandards von  NIST, WMO/CIMO, NWS/NOAA, ASTM und ISO zu treffen, offensichtlich.

Das klarste Beispiel für die Notwendigkeit, grundlegende Messstandards zu treffen, kann man in Überwachung des Stadtklimas finden, da die Städte eine Reihe von Herausforderungen an die genaue Messung der Lufttemperatur stellen. Die Fußwege und Gebäudewände in der Nähe von Wetterstationen reflektieren und strahlen Sonnenenergie viel stärker als Grasrasen und aus jeder Richtung auf einen Temperatursensor ab, was zu großen Fehlern bei der Lufttemperaturmessung führt. Da die Verteilung der Fehler bei der Lufttemperaturmessung nicht symmetrisch rund um tatsächlichen Temperaturwert und für jede Wetterstationsinstallation einzigartig ist, hat die Praxis gezeigt, dass sich die Qualität von Daten geringer Qualität nicht mit der Größe des Datensatzes verbessert.

Die Qualität der Lufttemperaturmessung kann leicht beurteilt werden, indem die Sonneneinstrahlung (W / m²) und die Lufttemperatur (° C / ° F) zusammen eingezeichnet werden. Lufttemperatursensoren niedriger Qualität zeigen zusammen mit billigen Sonnenschutzschildern eine Erhöhung der Lufttemperatur um +0,5 ° C (+1 ° F) oder mehr innerhalb weniger Minuten, nachdem die Sonne von hinter her Wolken oder die Wetterstation aus dem Schatten hervorkommt.

Manufacturer of high-quality and affordable meteorological solutions for Smart-City environmental sensor networks including the MeteoHelix IoT, MeteoRain IoT and MeteoWind IoT wireless weather station and sensors.

Scales of Meteorological Networks - How are Meteorological Networks Classified?

While the world of meteorology is changing fast, it is important to use and maintain correct terminology. The following meteorological network classification promotes clear and concise communication between professionals, researchers, amateur enthusiasts, and the public.

Meteorological observation networks are classified by their sensor and weather station spacing (how far apart are observations and measuring points from each other). With the advent of the Internet-of-Things (IoT), there is a strong focus on creating dense city-scale and local-scale weather station networks even at the expense of conforming to even the loosest measurement standards. 

Scientists seem to be forgetting that the promise of lower-cost wireless and sensor technologies does not, in fact, replace measurement know-how. This measurement know-how is a result of long-term experience and R&D, which takes time and, by default, drives up sensor costs of companies able to perform real measurements with scientific precision. Minimizing the "Observer Effect" of measurement systems takes know-how to avoid measuring unintended influences.  One such sin is mounting a rain bucket or a radiation shield close to an ultrasonic wind sensor. Cheap solar radiation shielding for air temperature sensors is another common mistake of amateurs and professionals alike. It is described in detail in "Will the BARANI DESIGN MeteoShield® replace the Stevenson screen as the new reference for climate change measurements?"

• Royal Belgian Meteorological Institute proves "The New Standard in Air Temperature Measurement is the BARANI helical MeteoShield®"

• Smart-City Mistakes to Avoid: The Question of Big-Data vs. Accurate-Data

• Big data vs. accurate data

Types of meteorological networks and their density.
Spatial scale areal extent	Description	Atmospheric processes and applications	Network examples
Global-scale 1,000+ km	Global network of networks, internationally coordinated and facilitated	Semi-permanent pressure centers like the Polar Vortex and trade winds. Data is used from synoptic forecasting, global climate change monitoring and modeling, satellite sensor calibration and validation	Global surface temperature monitoring networks such as NOAA Global Historical Climate Network (GHCN) and Global Climate Observing System (GCOS)
Synoptic Macro-scale 100 km - 1,000 km	Networks of national meteorological monitoring stations located within countries, usually in rural areas. Used for examining regional and national synoptic events	National weather forecasting (extratropical cyclones, baroclinic troughs and ridges, frontal zones), modeling	US Automated Weather Observing System (AWOS), US Climate Reference Network (USCRN), AMeDAS, Japan, and the UK Met Office MIDAS network have stations in rural and urban areas that provide hourly surface weather data for weather forecasting, aviation. These datasets are also fed into global data networks
Meso-scale Mesonet Regional network 10 km - 100 km	Monitor regional meso-scale weather events. Cover urban, peri-urban and rural areas. Meso-scale meteorological events are often hazardous and might go undetected without densely spaced weather observations. Individual monitoring equipment representative of the local or micro-scale climate meso-scale measurements from individual sensors is only now becoming possible with the advent of WMO precision micro-weather stations like the MeteoHelix.	Thunderstorms, downbursts, squall lines, temperature variations over urban and rural areas, sea circulations	Currently several relatively high-density Mesonets (meso-scale networks) exist in the US, China, Finland like the Oklahoma Mesonet network which was designed and implemented by scientists as the gold standard for mesonets by the University of Oklahoma (OU) and Oklahoma State University (OSU). For more information about Mesonets across the United States, visit the National Mesonet website.
City-scale 1 km - 10 km	Monitoring weather and climate at the scale of the whole city. Individual monitoring equipment representative of the local micro-climate. City-scale measurements from individual sensors are now becoming possible with the advent of WMO precision micro-weather stations like the MeteoHelix.	Urban heat island studies, urban climate studies, air pollution	Very high weather station density networks such as the Oklahoma City Micronet, installed to examine urban climate variability.
Local-scale Neighbourhood 100 meters - 1 km	Effects of minor landscape features (parks, ponds, small topographic features) neighbourhoods with similar types of urban development (surface cover, size and spacing of buildings, activity). Meteorological equipment can be mounted on street lamp posts and is sited to be representative of neighbourhood (i.e. a set height, representative surface cover, little obstructions, to avoid micro-climate effects)	Urban heat island, variations with land use, surface cover, air pollution, tornadoes and twisters	Few local-scale networks exists, since most individual climate stations within city-scale networks or meso-scale networks are often representative of the neighbourhood in which it is located (unless they are specifically examining micro-climates). Urban networks are usually city-scale or meso-scale since dense networks are not necessary to assess local-scale climate over similar land-use types
Micro-scale 100 meters or less	Micrometeorological phenomena. Influenced by urban areas, the dimensions of component elements: buildings, green roofs, trees, roads, streets, courtyards, and gardens. Equipment such as a micro-weather station can be located on street lamps or traffic light poststo be representative of the micro-climate	Urban canyon studies, turbulence and dispersion studies, human comfort and exposure, impact of buildings, agricultural meteorology	Some micro-scale networks such as uScan project, Tokyo, have been used to examine fine-scale temperature variations over complex infrastructure
Citation	Muller, C.L., Chapman, L., Grimmond, C.S.B., Young, D.T. and Cai, X. (2013), Sensors and the city: a review of urban meteorological networks. Int. J. Climatol., 33: 1585-1600. doi:10.1002/joc.3678   https://rmets.onlinelibrary.wiley.com/doi/full/10.1002/joc.3678

MANUFACTURER WMO PRECISION MICRO-WEATHER STATIONS FOR MESONETS, MICRONETS AND OTHER PROFESSIONAL METEOROLOGICAL SENSORS.