Mesonet

Last updated
A weather map consisting of a station model plot of Oklahoma Mesonet data overlaid with WSR-88D weather radar data depicting possible horizontal convective rolls as a potential contributing factor in the incipient 3 May 1999 tornado outbreak A mobile mesonet also documented tornadic supercells and their immediate environments during this event. HCR OK Mesonet.gif
A weather map consisting of a station model plot of Oklahoma Mesonet data overlaid with WSR-88D weather radar data depicting possible horizontal convective rolls as a potential contributing factor in the incipient 3 May 1999 tornado outbreak A mobile mesonet also documented tornadic supercells and their immediate environments during this event.

In meteorology and climatology, a mesonet, portmanteau of mesoscale network, is a network of automated weather and, often also including environmental monitoring stations, designed to observe mesoscale meteorological phenomena and/or microclimates. [3] [4]

Contents

Dry lines, squall lines, and sea breezes are examples of phenomena observed by mesonets. Due to the space and time scales associated with mesoscale phenomena and microclimates, weather stations comprising a mesonet are spaced closer together and report more frequently than synoptic scale observing networks, such as the WMO Global Observing System (GOS) and US ASOS. The term mesonet refers to the collective group of these weather stations, which are usually owned and operated by a common entity. Mesonets generally record in situ surface weather observations but some involve other observation platforms, particularly vertical profiles of the planetary boundary layer (PBL). [5] Other environmental parameters may include insolation and various variables of interest to particular users, such as soil temperature or road conditions (the latter notable in Road Weather Information System (RWIS) networks).

The distinguishing features that classify a network of weather stations as a mesonet are station density and temporal resolution with sufficiently robust station quality. Depending upon the phenomena meant to be observed, mesonet stations use a spatial spacing of 1 to 40 kilometres (0.6 to 20 mi) [6] and report conditions every 1 to 15 minutes. Micronets (see microscale and storm scale), such as in metropolitan areas such as Oklahoma City, [7] St. Louis, and Birmingham UK, are yet denser in spatial and sometimes temporal resolution. [8]

Purpose

Thunderstorms and other atmospheric convection, squall lines, drylines, [9] sea and land breezes, mountain breeze and valley breezes, mountain waves, mesolows and mesohighs, wake lows, mesoscale convective vortices (MCVs), tropical cyclone and extratropical cyclone rainbands, macrobursts, gust fronts and outflow boundaries, heat bursts, urban heat islands (UHIs), and other mesoscale phenomena, as well as topographical features, can cause weather and climate conditions in a localized area to be significantly different from that dictated by the ambient large-scale conditions. [10] [11] As such, meteorologists must understand these phenomena in order to improve forecast skill. Observations are critical to understanding the processes by which these phenomena form, evolve, and dissipate.

The long-term observing networks (ASOS, AWOS, COOP), however, are too sparse and report too infrequently for mesoscale research and forecasting. ASOS and AWOS stations are typically spaced 50 to 100 kilometres (30 to 60 mi) apart and report only hourly at many sites (though over time the frequency of reporting has increased, down to 5-15 minutes in the 2020s at major sites). The Cooperative Observer Program (COOP) database consists of only daily reports recorded manually. That network, like the more recent CoCoRaHS, is large but both are limited in reporting frequency and robustness of equipment. "Mesoscale" weather phenomena occur on spatial scales of a few to hundreds of kilometers and temporal (time) scales of minutes to hours. Thus, an observing network with finer temporal and spatial scales is needed for mesoscale research. This need led to the development of the mesonet.

Mesonet data is directly used by humans for decision making, but also boosts the skill of numerical weather prediction (NWP) and is especially beneficial for short-range mesoscale models. Mesonets, along with remote sensing solutions (data assimilation of weather radar, weather satellites, wind profilers), allow for much greater temporal and spatial resolution in a forecast model. As the atmosphere is a chaotic nonlinear dynamical system (i.e. subject to the Butterfly effect), this increase in data increases understanding of initial conditions and boosts model performance. In addition to meteorology and climatology users, hydrologists, foresters, wildland firefighters, transportation departments, energy producers and distributors, other utility interests, and agricultural entities are prominent in their need for fine scale weather information. These organizations operate dozens of mesonets within the US and globally. Environmental, outdoor recreational, emergency management and public safety, military, and insurance interests also are heavy users of mesonet information.

In many cases, mesonet stations may, by necessity or sometimes by lack of awareness, be located in positions where accurate measurements may be compromised. For instance, this is especially true of citizen science and crowdsourced data systems, such as the stations built for WeatherBug's network, many of which are located on school buildings. The Citizen Weather Observer Program (CWOP) facilitated by the US National Weather Service (NWS) and other networks such as those collected by Weather Underground help fill gaps with resolutions sometimes meeting or exceeding that of mesonets, but many stations also exhibit biases due to improper siting, calibration, and maintenance. These consumer grade "personal weather stations" (PWS) are also less sensitive and rigorous than scientific grade stations. The potential bias that these stations may cause must be accounted for when ingesting the data into a model, lest the phenomenon of "garbage in, garbage out" occur.

Operations

Kentucky Mesonet station WSHT near Maysville in Mason County WSHT.jpg
Kentucky Mesonet station WSHT near Maysville in Mason County

Mesonets were born out of the need to conduct mesoscale research. The nature of this research is such that mesonets, like the phenomena they were meant to observe, were (and sometimes still are) short-lived and may change rapidly. Long-term research projects and non-research groups, however, have been able to maintain a mesonet for many years. For example, the U.S. Army Dugway Proving Ground in Utah has maintained a mesonet for many decades. The research-based origin of mesonets led to the characteristic that mesonet stations may be modular and portable, able to be moved from one field program to another. Nonetheless, most large contemporary mesonets or nodes within consist of permanent stations comprising stationary networks. Some research projects, however, utilize mobile mesonets. Prominent examples include the VORTEX projects. [12] [13] The problems of implementing and maintaining robust fixed stations are exacerbated by lighter, compact mobile stations and are further worsened by various issues related when moving, such as vehicle slipstream effects, and particularly during rapid changes in the ambient environment associated with traversing severe weather. [14]

Whether the mesonet is temporary or semi-permanent, each weather station is typically independent, drawing power from a battery and solar panels. An on-board computer records readings from several instruments measuring temperature, humidity, wind speed and direction, and atmospheric pressure, as well as soil temperature and moisture, and other environmental variables deemed important to the mission of the mesonet, solar irradiance being a common non-meteorological parameter. The computer periodically saves these data to memory, typically using data loggers, and transmits the observations to a base station via radio, telephone (wireless, such as cellular or landline), or satellite transmission. Advancements in computer technology and wireless communications in recent decades made possible the collection of mesonet data in real-time. Some stations or networks report using Wi-Fi and grid powered with backups for redundancy.

The availability of mesonet data in real-time can be extremely valuable to operational forecasters, and particularly for nowcasting, [15] as they can monitor weather conditions from many points in their forecast area. In addition to operational work, and weather, climate, and environmental research, mesonet and micronet data are often important in forensic meteorology. [16]

History

Three-day barograph of the type used by the Meteorological Service of Canada Barograph.JPG
Three-day barograph of the type used by the Meteorological Service of Canada

Early mesonets operated differently from modern mesonets. Each constituent instrument of the weather station was purely mechanical and fairly independent of the other sensors. Data were recorded continuously by an inked stylus that pivoted about a point onto a rotating drum covered by a sheath of graphed paper called a trace chart, much like a traditional seismograph station. Data analysis could occur only after the trace charts from the various instruments were collected.

One of the earliest mesonets operated in the summer of 1946 and 1947 and was part of a field campaign called The Thunderstorm Project. [17] As the name implies, the objective of this program was to better understand thunderstorm convection. The earliest mesonets were typically funded and operated by government agencies for specific campaigns. In time, universities and other quasi-public entities began implementing permanent mesonets for a wide variety of uses, such as agricultural or maritime interests. Consumer grade stations added to the professional grade synoptic and mesoscale networks by the 1990s and by the 2010s professional grade station networks operated by private companies and public-private consortia increased in prominence. Some of these privately implemented systems are permanent and at fixed locations, but many also service specific users and campaigns/events so may be installed for limited periods, and may also be mobile.

The first known mesonet was operated by Germany from 1939 to 1941. Early mesonets with project based purposes operated for limited periods of time from seasons to a few years. The first permanently operating mesonet began in the United States in the 1970s with more entering operation in the 1980s-1990s as numbers gradually increased preceding a steeper expansion by the 2000s. By the 2010s there was also an increase in mesonets on other continents. Some wealthy densely populated countries also deploy observation networks with the density of a mesonet, such as the AMeDAS in Japan. The US was an early adopter of mesonets, yet funding has long been scattered and meager. By the 2020s declining funding atop the earlier scarcity and uncertainty of funding was leading to understaffing and problems maintaining stations, the closure of some stations, and the viability of entire networks threatened. [18]

Mesonets capable of being moved for fixed station deployments in field campaigns came into use in the US by the 1970s [19] and fully mobile vehicle-mounted mesonets became fixtures of large field research projects following the field campaigns of Project VORTEX in 1994 and 1995, in which significant mobile mesonets were deployed.

Significant mesonets

The following table is an incomplete list of mesonets operating in the past and present:

Years of operationName of Network, PlaceSpacingNo. of Stations
(Year)		Objectives
1939-41 Lindenberger Böennetz  [ de ], Lindenberg  [ de ], Tauche, Germany3–20 km (1.9–12.4 mi)19-25research on convective hazards, including squall lines and wind gusts, to aviation [11]
1940 Maebashi, Japan8–13 km (5.0–8.1 mi)20
(1940)		research on convective hazards to aviation, examined structure of thunderstorms [11]
1941 Muskingum basin, Ohio 10 km (6.2 mi)131
(1941)		rainfall and runoff research [11]
1946The Thunderstorm Project, Florida 1 mi (1.6 km)50
(1946)		thunderstorm convection research [20]
1947The Thunderstorm Project, Ohio2 mi (3.2 km)58
(1947)		thunderstorm convection research [20]
1960New Jersey10 km (6.2 mi)23
(1960)		research on mesoscale pressure systems [11]
1960 Fort Huachuca, Arizona20 km (12 mi)28
(1960)		Army operations (military meteorology) research [11]
1961Fort Huachuca, Arizona3 km (1.9 mi)17
(1961)		research on influence of orography [11]
1961–Present Dugway Proving Ground, Utah 9 mi (14 km)26air quality modeling and other desert area research
1961Flagstaff, Arizona8 km (5.0 mi)43
(1961)		 cumulonimbus convection research [11]
1961National Severe Storms Project (NSSP), Southern Plains US20 km (12 mi)36
(1961)		research on structure of severe storms [11] [21]
1962National Severe Storms Project (NSSP), Southern Plains US60 km (37 mi)210
(1962)		research on squall lines and pressure jumps [11]
1961–1980s [22] NSSL mesonetwork and mesometeorological rawinsonde networks, Oklahoma<6-17 mi (<9-28 km) surface, 18-53 mi (30-85 km) upper (1966-1970)30-61 surface, 8-11 upper (1966-1970)primarily convection and dryline research in partnership with AF and Army, with focus in some years on aviation and particularly airport operations; annual field projects included varying number and spatial density of seasonal surface and upper air stations combined with radar and aircraft observations plus instrumented tower, leading to evolution of storm scale networks [23] [24] and automated networks (e.g. NSSL Surface Automated Mesonetwork); other research projects increasingly arose in 70s-80s
1972–PresentEnviro-Weather, Michigan (now also adjacent sections of Wisconsin)Varies81agriculturally centered; archive, varies from 5-60 min observations [25]
1976-1982
1982-1987		NCAR Portable Automated Mesonet I
NCAR Portable Automated Mesonet II		30 [19]
≈200 [26] 		research networks
1981–PresentNebraska Mesonet, Nebraska Varies69
(2018)		originally agriculturally centered now multipurpose; archive, near real-time observations [27] [28] [29]
1983–PresentSouth Dakota Mesonet, South Dakota Varies27archive, real-time 5 min observations [30]
1984-1986+FAA-Lincoln Laboratory Operational Weather Studies (FLOWS)30aviation research network focused on low-level wind shear and microburst hazards with radar (TDWR) and other detection systems that became LLWAS [31]
1986–PresentKansas Mesonet, Kansas Varies72archive, real-time observations [32]
1986–PresentArizona Meteorological Network (AZMET), Arizona Varies27agriculturally centered; archive, real-time observations, 15 min - 1 hr [33]
1988–PresentWashington Mesonet/AgWeatherNet, Washington Varies177multi-network system (comprehensive monitoring, agricultural focused); archive, real-time observations, 5 and 15 min [34] [35]
1989–PresentOhio Agricultural Research and Development Center (OARDC) Weather System, Ohio Varies17agriculturally centered; archive, hourly observations [36]
1990–PresentNorth Dakota Agricultural Weather Network (NDAWN), North Dakota (also adjacent areas of NW-Minnesota and NE-Montana)Varies91agriculturally centered; archive, real-time observations [37]
1991–Present Oklahoma Mesonet, Oklahoma Varies121comprehensive monitoring; archive, real-time observations [38] [39]
1991–PresentGeorgia Automated Weather Network (AEMN), Georgia Varies82agriculture and hydrometeorology; archive, real-time observations, 15 min [40] [41]
1992-Present [42] Colorado Agricultural Meteorological Network (CoAgMet), Colorado agriculturally centered; 5 min data, archived [43]
1993–PresentMissouri Mesonet, Missouri Varies35agriculturally centered; archive, real-time observations at 21 stations [44] [45]
1994–Present WeatherBug (AWS), across United StatesVaries>8,000 **real-time observations for schools and television stations; collection of multiple mesonets, each typically centered around a host television station's media market [46] [47]
1997–PresentFlorida Automated Weather Network (FAWN), Florida Varies42agriculturally-centered; archive, real-time [48] [49]
1999–PresentWest Texas Mesonet, West Texas Varies63+archive, real-time observations [50] [51]
2001–PresentIowa Environmental Mesonet, Iowa Varies469*archive, real-time observations [52] [53]
-PresentWeatherFlow, global but concentrated in USVaries450+ mesonet stations in proprietary network; 27,000 in total * **real-time and archive for variety of purposes, proprietary but reports to public forecasters and numerical modeling systems; operates specialty mesonets and offers PWSs [54]
2002–PresentSolutions Mesonet, Eastern Canada Varies600+ *archive, real-time observations [55]
2002–PresentWestern Turkey Mesonet, Turkey Varies206+ nowcasting, hydrometeorology [56]
2003–PresentDelaware Environmental Observing System (DEOS), Delaware Varies57archive, real-time observations [57] [58]
2004–PresentSouth Alabama Mesonet (USA Mesonet), Alabama Varies26archive, real-time observations [59]
2004-2010 Foothills Climate Array (FCA), southern Alberta 10 km (6.2 mi) average300research on spatial-temporal meteorological variation, and on weather and climate model performance, across adjoining mountain, foothills, and prairie topographies [60]
2007–PresentKentucky Mesonet, Kentucky Varies68archive, real-time observations [61] [62] [63]
2007-PresentMount Washington Regional Mesonet, New Hampshire 18
(2022)		archive, near-real time observations primarily for orography, operated by Mount Washington Observatory [64] [65] [66]
2008–PresentQuantum Weather Mesonet, St. Louis metropolitan area, MissouriVaries (average ~5 miles (8.0 km))100 (proprietary)utility and nowcasting; archive, real-time observations [67]
-PresentNorth Carolina ECONet, North Carolina Varies99archive, real-time observations [68]
2010-PresentWeather Telenatics, North America Varies(proprietary)real-time and archived, proprietary; operates micronets, focused on ground transportation and airports but also serves other uses [69]
2012–PresentBirmingham Urban Climate Laboratory (BUCL) Mesonet, Birmingham UK3 per 1 km2 (0.4 sq mi)24urban heat island (UHI) monitoring [70] [71]
2015–PresentNew York State Mesonet, New York Varies, averages 20 miles (32 km)126real-time observations, improved forecasting [72]
2016–PresentTexMesonet, Texas Varies100 in network; 3,151 total * **hydrometeorology and hydrology focused network operated by the Texas Water Development Board, plus network of networks; some real-time observations, archival [73]
-PresentNew Jersey Weather & Climate Network (NJWxNet), New Jersey Varies66real-time observations [74]
-PresentKeystone Mesonet, Pennsylvania Variesreal-time observations, archived; variety of uses, network of networks [75]
-PresentCape Breton Mesonet, Cape Breton Island, with some stations in Newfoundland, Prince Edward Island, and mainland Nova Scotia Varies141+real-time observations, with archived data available. [76]
2019-presentCOtL (Conditions Over the Landscape) Mesonet, South Australia agriculturally focused with a particular emphasis on monitoring amenability of weather conditions for crop spraying; a merger of Mid North Mesonet that began operating in 2019 and Riverland & Mallee Mesonet which began in 2021 with additional networks anticipated [77]
≈2020-PresentUmbria region mesonet, Umbria, Central Italy Variesnetwork of preexisting networks emerging since 2020 in part to monitor complex topography but with various purposes for constituent networks [78]
2022-PresentHawai'i Mesonet, Hawaiian Islands Varies>95
(2022)		near real-time observations with archives, [79] for a variety of weather and climate uses designed to measure the stark microclimates of Hawaii [80] and as an expansion to local micronets such as HaleNet, HavoNet, HIPPNET, and CraterNet [81]
In developmentWisconsin Environmental Mesonet (Wisconet), Wisconsin90near real-time observations with archives, agriculturally focused [82]

* Not all stations owned or operated by network.
** As these are private stations, although QA/QC measures may be taken, these may not be scientific grade, and may lack proper siting, calibration, sensitivity, durability, and maintenance.

Although not labeled a mesonet, the Japan Meteorological Agency (JMA) also maintains a nationwide surface observation network with the density of a mesonet. JMA operates AMeDAS, consisting of approximately 1,300 stations at a spacing of 17 kilometres (11 mi). The network began operating in 1974. [83]

See also

Related Research Articles

<span class="mw-page-title-main">Thunderstorm</span> Type of weather with lightning and thunder

A thunderstorm, also known as an electrical storm or a lightning storm, is a storm characterized by the presence of lightning and its acoustic effect on the Earth's atmosphere, known as thunder. Relatively weak thunderstorms are sometimes called thundershowers. Thunderstorms occur in a type of cloud known as a cumulonimbus. They are usually accompanied by strong winds and often produce heavy rain and sometimes snow, sleet, or hail, but some thunderstorms produce little precipitation or no precipitation at all. Thunderstorms may line up in a series or become a rainband, known as a squall line. Strong or severe thunderstorms include some of the most dangerous weather phenomena, including large hail, strong winds, and tornadoes. Some of the most persistent severe thunderstorms, known as supercells, rotate as do cyclones. While most thunderstorms move with the mean wind flow through the layer of the troposphere that they occupy, vertical wind shear sometimes causes a deviation in their course at a right angle to the wind shear direction.

<span class="mw-page-title-main">Weather station</span> Facility for atmospheric research and prediction

A weather station is a facility, either on land or sea, with instruments and equipment for measuring atmospheric conditions to provide information for weather forecasts and to study the weather and climate. The measurements taken include temperature, atmospheric pressure, humidity, wind speed, wind direction, and precipitation amounts. Wind measurements are taken with as few other obstructions as possible, while temperature and humidity measurements are kept free from direct solar radiation, or insolation. Manual observations are taken at least once daily, while automated measurements are taken at least once an hour. Weather conditions out at sea are taken by ships and buoys, which measure slightly different meteorological quantities such as sea surface temperature (SST), wave height, and wave period. Drifting weather buoys outnumber their moored versions by a significant amount.

<span class="mw-page-title-main">Weather radar</span> Radar used to locate and monitor meteorological conditions

Weather radar, also called weather surveillance radar (WSR) and Doppler weather radar, is a type of radar used to locate precipitation, calculate its motion, and estimate its type. Modern weather radars are mostly pulse-Doppler radars, capable of detecting the motion of rain droplets in addition to the intensity of the precipitation. Both types of data can be analyzed to determine the structure of storms and their potential to cause severe weather.

<span class="mw-page-title-main">Numerical weather prediction</span> Weather prediction using mathematical models of the atmosphere and oceans

Numerical weather prediction (NWP) uses mathematical models of the atmosphere and oceans to predict the weather based on current weather conditions. Though first attempted in the 1920s, it was not until the advent of computer simulation in the 1950s that numerical weather predictions produced realistic results. A number of global and regional forecast models are run in different countries worldwide, using current weather observations relayed from radiosondes, weather satellites and other observing systems as inputs.

<span class="mw-page-title-main">Tropical cyclone forecast model</span> Computer program that uses meteorological data to forecast tropical cyclones

A tropical cyclone forecast model is a computer program that uses meteorological data to forecast aspects of the future state of tropical cyclones. There are three types of models: statistical, dynamical, or combined statistical-dynamic. Dynamical models utilize powerful supercomputers with sophisticated mathematical modeling software and meteorological data to calculate future weather conditions. Statistical models forecast the evolution of a tropical cyclone in a simpler manner, by extrapolating from historical datasets, and thus can be run quickly on platforms such as personal computers. Statistical-dynamical models use aspects of both types of forecasting. Four primary types of forecasts exist for tropical cyclones: track, intensity, storm surge, and rainfall. Dynamical models were not developed until the 1970s and the 1980s, with earlier efforts focused on the storm surge problem.

<span class="mw-page-title-main">Atmospheric model</span> Mathematical model of atmospheric motions

In atmospheric science, an atmospheric model is a mathematical model constructed around the full set of primitive, dynamical equations which govern atmospheric motions. It can supplement these equations with parameterizations for turbulent diffusion, radiation, moist processes, heat exchange, soil, vegetation, surface water, the kinematic effects of terrain, and convection. Most atmospheric models are numerical, i.e. they discretize equations of motion. They can predict microscale phenomena such as tornadoes and boundary layer eddies, sub-microscale turbulent flow over buildings, as well as synoptic and global flows. The horizontal domain of a model is either global, covering the entire Earth, or regional (limited-area), covering only part of the Earth. The different types of models run are thermotropic, barotropic, hydrostatic, and nonhydrostatic. Some of the model types make assumptions about the atmosphere which lengthens the time steps used and increases computational speed.

In weather forecasting, model output statistics (MOS) is a multiple linear regression technique in which predictands, often near-surface quantities, are related statistically to one or more predictors. The predictors are typically forecasts from a numerical weather prediction (NWP) model, climatic data, and, if applicable, recent surface observations. Thus, output from NWP models can be transformed by the MOS technique into sensible weather parameters that are familiar to a layperson.

<span class="mw-page-title-main">Doppler on Wheels</span> Fleet of X-band radar trucks maintained by the Center for Severe Weather Research (CSWR)

Doppler on Wheels is a fleet of X-band and C-band radar mobile and quickly-deployable truck-borne radars which are the core instrumentation of the Flexible Array of Radars and Mesonets affiliated with the University of Illinois and led by Joshua Wurman, with the funding partially provided by the National Science Foundation (NSF). The DOW fleet and its associated Mobile Mesonets and deployable weather stations Doppler on Wheels (DOW) has been deploying in hazardous/challenging weather, driving into the eye of the storm to gather scientific data about wind, rain and snow that are missed by stationary radar systems. They are part of the NSF's "Community Instruments and Facilities" (CIF) program.

<span class="mw-page-title-main">Ron Przybylinski</span> American meteorologist

Ronald William Przybylinski was an American meteorologist who made important contributions to understanding of bow echoes, mesovortices, related quasi-linear convective system (QLCS) structures and processes, as well as QLCS related tornadoes. He also was an expert on technical aspects of weather radar and applications to both operational meteorology and research.

Convective storm detection is the meteorological observation, and short-term prediction, of deep moist convection (DMC). DMC describes atmospheric conditions producing single or clusters of large vertical extension clouds ranging from cumulus congestus to cumulonimbus, the latter producing thunderstorms associated with lightning and thunder. Those two types of clouds can produce severe weather at the surface and aloft.

<span class="mw-page-title-main">VORTEX projects</span> Field experiments that study tornadoes

The Verification of the Origins of Rotation in Tornadoes Experiment are field experiments that study tornadoes. VORTEX1 was the first time scientists completely researched the entire evolution of a tornado with an array of instrumentation, enabling a greater understanding of the processes involved with tornadogenesis. A violent tornado near Union City, Oklahoma was documented in its entirety by chasers of the Tornado Intercept Project (TIP) in 1973. Their visual observations led to advancement in understanding of tornado structure and life cycles.

<span class="mw-page-title-main">Wake low</span> Weather system

A wake low, or wake depression, is a mesoscale low-pressure area which trails the mesoscale high following a squall line. Due to the subsiding warm air associated with the system's formation, clearing skies are associated with the wake low. Once difficult to detect in surface weather observations due to their broad spacing, the formation of mesoscale weather station networks, or mesonets, has increased their detection. Severe weather, in the form of high winds, can be generated by the wake low when the pressure difference between the mesohigh preceding it and the wake low is intense enough. When the squall line is in the process of decay, heat bursts can be generated near the wake low. Once new thunderstorm activity along the squall line concludes, the wake low associated with it weakens in tandem.

MesoWest is a cooperative project to observe and archive mesoscale weather observations across the United States. Their observations include but are not limited to temperature, humidity, wind speed, wind direction, and precipitation. Their data is also known to be central for climate records, such as for monitoring microclimates.

An atmospheric reanalysis is a meteorological and climate data assimilation project which aims to assimilate historical atmospheric observational data spanning an extended period, using a single consistent assimilation scheme throughout.

A mesovortex is a small-scale rotational feature found in a convective storm, such as a quasi-linear convective system, a supercell, or the eyewall of a tropical cyclone. Mesovortices range in diameter from tens of miles to a mile or less and can be immensely intense.

The Oklahoma Mesonet is a network of environmental monitoring stations designed to measure the environment at the size and duration of mesoscale weather events. The phrase "mesonet" is a portmanteau of the words mesoscale and network.

<span class="mw-page-title-main">History of numerical weather prediction</span> Aspect of meteorological history

The history of numerical weather prediction considers how current weather conditions as input into mathematical models of the atmosphere and oceans to predict the weather and future sea state has changed over the years. Though first attempted manually in the 1920s, it was not until the advent of the computer and computer simulation that computation time was reduced to less than the forecast period itself. ENIAC was used to create the first forecasts via computer in 1950, and over the years more powerful computers have been used to increase the size of initial datasets and use more complicated versions of the equations of motion. The development of global forecasting models led to the first climate models. The development of limited area (regional) models facilitated advances in forecasting the tracks of tropical cyclone as well as air quality in the 1970s and 1980s.

The Jule G. Charney Award is the American Meteorological Society's award granted to "individuals in recognition of highly significant research or development achievement in the atmospheric or hydrologic sciences". The prize was originally known as the Second Half Century Award, and first awarded to mark to fiftieth anniversary of the society.

<span class="mw-page-title-main">Project NIMROD</span> Field study of severe thunderstorms and severe winds

Project NIMROD was a meteorological field study of severe thunderstorms and their damaging winds conducted by the National Center for Atmospheric Research (NCAR). It took place in the Greater Chicago area from May 15 to June 30, 1978. Data collected was from single cell thunderstorms as well as mesoscale convective systems, such as bow echoes. Using Doppler weather radars and damage clues on the ground, the team studied mesocyclones, downbursts and gust fronts. NIMROD was the first time that microbursts, very localized strong downdrafts under thunderstorms, were detected; this helped improve airport and public safety by the development of systems like the Terminal Doppler Weather Radar and the Low-level windshear alert system.

A weather drone, or weather-sensing uncrewed aerial vehicle (UAV), – is a remotely piloted aircraft weighing less than 25 kg and carrying sensors that collect thermodynamic and kinematic data from the mid and lower atmosphere.

References

  1. ↑ Edwards, Roger; R. L. Thompson; J. G. LaDue (Sep 2000). "Initiation of Storm A (3 May 1999) along a Possible Horizontal Convective Roll". 20th Conference on Severe Local Storms. Orlando, FL: American Meteorological Society. Retrieved 2022-04-29.
  2. ↑ Markowski, Paul M. (2002). "Mobile Mesonet Observations on 3 May 1999". Weather Forecast. 17 (3): 430–444. Bibcode:2002WtFor..17..430M. doi: 10.1175/1520-0434(2002)017<0430:MMOOM>2.0.CO;2 .
  3. ↑ "Mesonet". National Weather Service Glossary. National Weather Service. Retrieved 2017-03-30.
  4. ↑ Glickman, Todd S., ed. (2000). Glossary of Meteorology (2nd ed.). Boston: American Meteorological Society. ISBN   978-1-878220-34-9.
  5. ↑ Marshall, Curtis H. (11 Jan 2016). "The National Mesonet Program". 22nd Conference on Applied Climatology. New Orleans, LA: American Meteorological Society.
  6. ↑ Fujita, Tetsuya Theodore (1962). A Review of Researches on Analytical MesoMeteorology. SMRP Research Paper. Vol. #8. Chicago: University of Chicago. OCLC   7669634.
  7. ↑ Basara, Jeffrey B.; Illston, B. G.; Fiebrich, C. A.; Browder, P. D.; Morgan, C. R.; McCombs, A.; Bostic, J. P.; McPherson, R. A. (2011). "The Oklahoma City Micronet". Meteorological Applications . 18 (3): 252–61. doi: 10.1002/met.189 .
  8. ↑ Muller, Catherine L.; Chapman, L.; Grimmond, C. S. B.; Young, D. T.; Cai, X (2013). "Sensors and the City: A Review of Urban Meteorological Networks" (PDF). Int. J. Climatol. 33 (7): 1585–600. Bibcode:2013IJCli..33.1585M. doi:10.1002/joc.3678. S2CID   140648553.
  9. ↑ Pietrycha, Albert E.; E. N. Rasmussen (2004). "Finescale Surface Observations of the Dryline: A Mobile Mesonet Perspective". Weather and Forecasting. 19 (12): 1075–88. Bibcode:2004WtFor..19.1075P. doi: 10.1175/819.1 .
  10. ↑ Fujita, T. Theodore (1981). "Tornadoes and Downbursts in the Context of Generalized Planetary Scales". Journal of the Atmospheric Sciences. 38 (8): 1511–34. Bibcode:1981JAtS...38.1511F. doi: 10.1175/1520-0469(1981)038<1511:TADITC>2.0.CO;2 . ISSN   1520-0469.
  11. 1 2 3 4 5 6 7 8 9 10 Ray, Peter S., ed. (1986). Mesoscale Meteorology and Forecasting. Boston: American Meteorological Society. ISBN   978-0933876668.
  12. ↑ Straka, Jerry M.; E. N. Rasmussen; S. E. Fredrickson (1996). "A Mobile Mesonet for Finescale Meteorological Observations". Journal of Atmospheric and Oceanic Technology. 13 (10): 921–36. Bibcode:1996JAtOT..13..921S. doi: 10.1175/1520-0426(1996)013<0921:AMMFFM>2.0.CO;2 . ISSN   1520-0426.
  13. ↑ Wurman, Joshua; D. Dowell; Y. Richardson; P. Markowski; E. Rasmussen; D. Burgess; L. Wicker; H. Bluestein (2012). "The Second Verification of the Origins of Rotation in Tornadoes Experiment: VORTEX2". Bulletin of the American Meteorological Society. 93 (8): 1147–70. Bibcode:2012BAMS...93.1147W. doi: 10.1175/BAMS-D-11-00010.1 .
  14. ↑ Waugh, Sean M. (2021). "The "U-Tube": An Improved Aspirated Temperature System for Mobile Meteorological Observations, Especially in Severe Weather". J. Atmos. Ocean. Technol. 38 (9): 1477–1489. Bibcode:2021JAtOT..38.1477W. doi: 10.1175/JTECH-D-21-0008.1 . hdl: 11244/24679 . S2CID   134944456.
  15. ↑ Mueller, Cynthia K.; J. W. Wilson; N. A. Crook (1993). "The Utility of Sounding and Mesonet Data to Nowcast Thunderstorm Initiation". Weather Forecast. 8 (1): 132–146. Bibcode:1993WtFor...8..132M. doi: 10.1175/1520-0434(1993)008<0132:TUOSAM>2.0.CO;2 .
  16. ↑ Brotzge, Jerald A.; C. A. Fiebrich (2021). "Mesometeorological Networks". In Foken, Thomas (ed.). Springer Handbook of Atmospheric Measurements. Springer. pp. 1233–1245. doi:10.1007/978-3-030-52171-4_45. ISBN   978-3-030-52170-7. S2CID   243969231.
  17. ↑ "Overview of The Thunderstorm Project". NOAA. Retrieved 16 June 2017.
  18. ↑ Rembert, Elizabeth (21 February 2023). "Weather stations that provide critical climate data are threatened by unstable funding". St. Louis Public Radio. Harvest Public Media. Retrieved 2023-02-21.
  19. 1 2 Brock, F. V.; P. K. Govind (1977). "Portable Automated Mesonet in Operation". Journal of Applied Meteorology and Climatology. 16 (3): 299–310. Bibcode:1977JApMe..16..299B. doi: 10.1175/1520-0450(1977)016<0299:PAMIO>2.0.CO;2 .}
  20. 1 2 Byers, Horace R.; R. R. Braham Jr. (1949). The Thunderstorm: Final Report of the Thunderstorm Project. Washington, DC: U.S. Government Printing Office. OCLC   7944529.
  21. ↑ Fujita, Tetsuya Theodore (1961). "Index to the NSSP Surface Network". Mesoscale Meteorology Project (Research Paper #2). University of Chicago for US Weather Bureau.
  22. ↑ Barnes, Stanley L. (1974). "Mesonetwork Array: Its Effect on Thunderstorm Flow Resolution". NOAA Technical Memorandum (ERL NSSL 74). Norman, OK: NOAA National Severe Storms Laboratory. Retrieved 2024-03-16.
  23. ↑ Barnes, Stanley L.; James H. Henderson; Robert J. Ketchum (1971). "Rawinsonde observation and processing techniques at the National Severe Storms Laboratory". NOAA Technical Memorandum (ERL NSSL 53). Norman, OK: ESSA National Severe Storms Laboratory. Retrieved 2024-03-16.
  24. ↑ Fankhauser, J. C. (1969). "Convective Processes Resolved by a Mesoscale Rawinsonde Network". Journal of Applied Meteorology. 8 (5): 778–798. doi:10.1175/1520-0450(1969)008<0778:CPRBAM>2.0.CO;2.
  25. ↑ "Enviroweather". msu.edu. Retrieved 12 April 2017.
  26. ↑ Brock, Fred V.; George H. Saum; Steven R. Semmer (1986). "Portable Automated Mesonet II". Journal of Atmospheric and Oceanic Technology. 3 (4): 573–582. doi:10.1175/1520-0426(1986)003<0573:PAMI>2.0.CO;2.
  27. ↑ "Mesonet by NSCO". unl.edu. Retrieved 12 April 2017.
  28. ↑ Hubbard, Kenneth G.; N. J. Rosenberg; D. C. Nielsen (1983). "Automated Weather Data Network for Agriculture". Journal of Water Resources Planning and Management. 109 (3): 213–222. doi:10.1061/(ASCE)0733-9496(1983)109:3(213).
  29. ↑ Shulski, Martha; S. Cooper; G. Roebke; A. Dutcher (2018). "The Nebraska Mesonet: Technical Overview of an Automated State Weather Network". Journal of Atmospheric and Oceanic Technology. 35 (11): 2189–2200. Bibcode:2018JAtOT..35.2189S. doi: 10.1175/JTECH-D-17-0181.1 .
  30. ↑ "South Dakota Mesonet". sdstate.edu. Retrieved 12 June 2017.
  31. ↑ Wolfson, Marilyn M. (1989). "The FLOWS Automatic Weather Station Network". Journal of Atmospheric and Oceanic Technology. 6 (2): 307–326. doi:10.1175/1520-0426(1989)006<0307:TFAWSN>2.0.CO;2.
  32. ↑ "Kansas Mesonet". k-state.edu. Retrieved 12 April 2017.
  33. ↑ "AZMET: The Arizona Meteorological Network". arizona.edu. Retrieved 12 April 2017.
  34. ↑ "AgWeatherNet at Washington State University". wsu.edu. Retrieved 12 April 2017.
  35. ↑ Elliot, T.V. (2008). "Regional and on-farm wireless sensor networks for agricultural systems in Eastern Washington". Comput. Electron. Agr. 61 (1): 32–43. doi:10.1016/j.compag.2007.05.007.
  36. ↑ "OARDC Weather System". ohio-state.edu. Retrieved 12 April 2017.
  37. ↑ "NDAWN Current Weather". ndsu.nodak.edu. Retrieved 24 March 2017.
  38. ↑ "Mesonet". mesonet.org. Retrieved 7 February 2017.
  39. ↑ McPherson, Renee A.; C.A. Fiebrich; K.C. Crawford; J.R. Kilby; D.L. Grimsley; J.E. Martinez; J.B. Basara; B.G. Illston; D.A. Morris; K.A. Kloesel; A.D. Melvin; H. Shrivastava; J. Wolfinbarger; J.P. Bostic; D.B. Demko; R.L. Elliott; S.J. Stadler; J.D. Carlson; A.J. Sutherland (2007). "Statewide Monitoring of the Mesoscale Environment: A Technical Update on the Oklahoma Mesonet". Journal of Atmospheric and Oceanic Technology. 24 (3): 301–21. Bibcode:2007JAtOT..24..301M. doi: 10.1175/JTECH1976.1 . S2CID   124213569.
  40. ↑ "Georgia Weather - Automated Environmental Monitoring Network Page". uga.edu. Retrieved 12 April 2017.
  41. ↑ Hoogenboom, Gerrit; D.D. Coker; J.M. Edenfield; D.M. Evans; C. Fang (2003). "The Georgia Automated Environmental Monitoring Network: Ten Years of Weather Information for Water Resources Management". Proceedings of the 2003 Georgia Water Resources Conference. Athens, GA: University of Georgia.
  42. ↑ Tucker, Donna F. (1997). "Surface Mesonets of the Western United States". Bull. Am. Meteorol. Soc. 78 (7): 1485–1496. Bibcode:1997BAMS...78.1485T. doi:10.1175/1520-0477(1997)078<1485:SMOTWU>2.0.CO;2. hdl: 1808/15914 .
  43. ↑ Schumacher, Russ. "COAgMET". Colorado State University. Retrieved 2023-02-24.
  44. ↑ "Missouri Mesonet". missouri.edu. Retrieved 12 April 2017.
  45. ↑ Guinan, Patrick (2008-08-11). "Missouri's transition to a near real-time mesonet". 17th Conference on Applied Climatology. Whistler, BC, Canada: American Meteorological Society.
  46. ↑ "Extensive Weather Observations & Analytics". earthnetworks.com. Retrieved 12 April 2017.
  47. ↑ Anderson, James E.; J. Usher (2010). "Mesonet Programs" (PDF). WMO Technical Conference on Meteorological and Environmental Instruments and Methods of Observation (TECO-2010). Helsinki: World Meteorological Organization.
  48. ↑ "FAWN - Florida Automated Weather Network". ufl.edu. Retrieved 12 April 2017.
  49. ↑ Lusher, William R.; Jackson, John L.; Morgan, Kelly T. (2008). "The Florida Automated Weather Network: Ten Years of Providing Weather Information to Florida Growers". Proc. Fla. State Hort. Soc. 121: 69–74.
  50. ↑ "West Texas Mesonet". Texas Tech University. Retrieved 7 February 2017.
  51. ↑ Schroeder, John L.; W.S. Burgett; K.B. Haynie; I.I. Sonmez; G.D. Skwira; A.L. Doggett; J.W. Lipe (2005). "The West Texas Mesonet: A Technical Overview". Journal of Atmospheric and Oceanic Technology. 22 (2): 211–22. Bibcode:2005JAtOT..22..211S. doi: 10.1175/JTECH-1690.1 .
  52. ↑ Herzmann, Daryl. "Iowa Environmental Mesonet". iastate.edu. Retrieved 7 February 2017.
  53. ↑ Todey, Dennis P.; E. S. Takle; S. E. Taylor (2002-05-13). "The Iowa Environmental Mesonet". 13th Conference on Applied Climatology and 10th Conference on Aviation, Range, and Aerospace Meteorology. Portland, Oregon: American Meteorological Society.
  54. ↑ "WeatherFlow Networks". WeatherFlow. Retrieved 2022-04-24.
  55. ↑ "Solutions Mesonet". Solutions Mesonet. 2019-04-12.
  56. ↑ Sönmez, İbrahim (2013). "Quality control tests for western Turkey Mesonet". Meteorological Applications. 20 (3): 330–7. Bibcode:2013MeApp..20..330S. doi: 10.1002/met.1286 .
  57. ↑ "DEOS Home". udel.edu. Retrieved 7 February 2017.
  58. ↑ Legates, David R.; D. J. Leathers; T. L. DeLiberty; G. E. Quelch; K. Brinson; J. Butke; R. Mahmood; S. A. Foster (2005-01-13). "DEOS: The Delaware Environmental Observing System". 21st International Conference on Interactive Information Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology. San Diego: American Meteorological Society.
  59. ↑ "CHILI - Center for Hurricane Intensity and Landfall Investigation". chiliweb.southalabama.edu. Retrieved 2019-09-14.
  60. ↑ Roberts, David R.; W. H, Wood; S. J. Marshall (2019). "Assessments of downscaled climate data with a high-resolution weather station network reveal consistent but predictable bias". Int. J. Climatol. 39 (6): 3091–3103. Bibcode:2019IJCli..39.3091R. doi:10.1002/joc.6005. S2CID   134732294.
  61. ↑ "Kentucky Mesonet". kymesonet.org. Retrieved 7 February 2017.
  62. ↑ Grogan, Michael; S. A. Foster; R. Mahmood (2010-01-21). "The Kentucky Mesonet". 26th Conference on Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology. Atlanta, Georgia: American Meteorological Society.
  63. ↑ Mahmood, Rezaul; M. Schargorodski; S. Foster; A. Quilligan (2019). "A Technical Overview of the Kentucky Mesonet". Journal of Atmospheric and Oceanic Technology. 36 (9): 1753–1771. Bibcode:2019JAtOT..36.1753M. doi: 10.1175/JTECH-D-18-0198.1 .
  64. ↑ "Mount Washington Regional Observatory". Mount Washington Observatory. Retrieved 2022-05-28.<
  65. ↑ Garrett, Keith (2020). "Robust Solutions to Maintaining the Mount Washington Regional Mesonet through Extreme Weather Conditions". 20th Symposium on Meteorological Observation and Instrumentation Joint Session with the National Network of Networks Committee. Boston, MA: American Meteorological Society.
  66. ↑ Fitzgerald, Brian J.; J. Broccolo; K. Garrett (2023). "The Mount Washington Observatory Regional Mesonet: A Technical Overview of a Mountain-Based Mesonet". J. Atmos. Ocean. Technol. 40 (4): 439–453. doi:10.1175/JTECH-D-22-0054.1.
  67. ↑ "Ameren website". ameren.com. Archived from the original on 16 March 2014. Retrieved 7 February 2017.
  68. ↑ "North Carolina Environment and Climate Observing Network". State Climate Office of North Carolina. Retrieved 7 February 2017.
  69. ↑ "Weather Telematics". Weather Telematics. Retrieved 2022-04-24.
  70. ↑ Chapman, Lee; Muller, C.L.; Young, D.T.; Warren, E.L.; Grimmond C.S.B.; Cai, X.-M.; Ferranti, J.S. (2015). "The Birmingham Urban Climate Laboratory: An Open Meteorological Test Bed and Challenges of the Smart City" (PDF). Bulletin of the American Meteorological Society. 96 (9): 1545–60. Bibcode:2015BAMS...96.1545C. doi:10.1175/BAMS-D-13-00193.1. S2CID   26884748.
  71. ↑ Warren, Elliot L.; D. T. Young; L. Chapman; C. Muller; C.S.B. Grimmond; X.-M. Cai (2016). "The Birmingham Urban Climate Laboratory—A high density, urban meteorological dataset, from 2012–2014". Scientific Data. 3 (160038): 160038. Bibcode:2016NatSD...360038W. doi:10.1038/sdata.2016.38. PMC   4896132 . PMID   27272103.
  72. ↑ "NYS Mesonet". nysmesonet.org. Retrieved 7 February 2017.
  73. ↑ "TexMesonet" . Retrieved 23 February 2020.
  74. ↑ "New Jersey Weather and Climate Network". njweather.org. Retrieved 12 April 2017.
  75. ↑ "Keystone Mesonet" . Retrieved 21 February 2020.
  76. ↑ "Cape Breton Mesonet" . Retrieved 22 January 2022.
  77. ↑ "COtL" . Retrieved 2023-02-24.
  78. ↑ Silvestri, Lorenzo; M. Saraceni; P. B. Cerlini (2022). "Quality management system and design of an integrated mesoscale meteorological network in Central Italy". Meteorol. Appl. 29 (2): e2060. doi: 10.1002/met.2060 . S2CID   248221267.
  79. ↑ "Hawai'i Mesonet". Hawai'i Climate Data Portal. University of Hawai'i. 2022. Retrieved 2022-04-24.
  80. ↑ Longman, Ryan J.; A. G. Frazier; A. J. Newman; T. W. Giambelluca; D. Schanzenbach; A. Kagawa-Viviani; H. Needham; J. R. Arnold; M. P. Clark (2019). "High-Resolution Gridded Daily Rainfall and Temperature for the Hawaiian Islands (1990–2014)". J. Hydrometeorol. 20 (3): 489–508. Bibcode:2019JHyMe..20..489L. doi: 10.1175/JHM-D-18-0112.1 . S2CID   134742459.
  81. ↑ "Climate Monitoring History". Hawai'i Climate Data Portal. University of Hawai'i. 2022. Retrieved 2022-04-24.
  82. ↑ Kremer, Rich (16 December 2022). "Federal grant to spur construction of weather, soil monitoring network to aid Wisconsin farmers". Wisconsin Public Radio. Retrieved 2023-02-24.
  83. ↑ "Japan Meteorological Agency". jma.go.jp. Retrieved 7 February 2017.
This page is based on this Wikipedia article
Text is available under the CC BY-SA 4.0 license; additional terms may apply.
Images, videos and audio are available under their respective licenses.