ALLAHABAD: A brief spell of scattered
rainfall on Wednesday ended a week-long heat spell in Allahabad. While eastern and northern parts of the city witnessed a downpour, western, southern and central pockets had to make do with a drizzle.
The around 20-minute bout of rainfall kept the Celsius on leash. The maximum temperature was 30.6 degrees and the minimum 28.2 degrees. The Met office
recorded 26mm rain till early evening. By night, several parts of the city were flooded following another spell of heavy rain.
Areas such as Telirganj, Govindpur, Mumfordganj, Katra and Old Cantonment soaked in the shower of joy while Civil Lines, Chowk, Mutthiganj, Kydganj and Khuladabad were left wanting for more.
Incidentally, when monsoon
had lashed the city earlier this month, its western fringes like Bamrauli, Sulem Sarai, Preetam Nagar, Rajroopur, Kareli and Dariyabad had received 13mm rainfall while the central and northern pockets had remained dry. The impact of scattered rainfall is more pronounced because of vegetation. While Old Cantonment, New Cantonment and their adjoining areas get abundant showers, those dominated by concrete jungle receive scanty rainfall.
“The phenomenon is directly proportionate to concretisation of the city and increasing pollution levels. Heat domes and pollution zones, along with varied vegetative cover and direction of winds, decide which area will receive what amount of rain,” said weather expert and AU professor SS Ojha. Every city has scattered vegetation these days and the same decides how much rainfall it may receive, echoed professor Savindra Singh, former head of the same department. “Areas on the riverfront will also get more rainfall as clouds collect more moisture there,” he explained.


Vertebrates are a well-known group of animals that includes mammals, birds, reptiles, amphibians, and fish. The defining characteristic of vertebrates is their backbone, an anatomical feature that first appeared in the fossil record about 500 million years ago, during the Ordovician period. In this article, we'll look at the various groups of vertebrates in the order in which they evolved to create a picture of how vertebrate evolution unfolded to the present day.
Jawless Fish (Class Agnatha)
The first vertebrates were the jawless fish (Class Agnatha). These fish-like animals had hard bony plates that covered their bodies and as their name implies, they did not have jaws. Additionally, these early fish did not have paired fins. The jawless fish are thought to have relied on filter feeding to capture their food, and most likely would have sucked water and debris from the seafloor into their mouth, releasing water and waste out of their gills.
The jawless fish that lived during the Ordovician period all went extinct by the end of the Devonian period. Yet today there are some species of fish that lack jaws (such as lampreys, and hagfish). These modern day jawless fish are not direct survivors of the Class Agnatha but are instead distant cousins of the cartilaginous fish.
Armored Fish (Class Placodermi)
The armored fish evolved during the Silurian period. Like their predecessors, they too lacked jaw bones but possessed paired fins. The armored fish diversified during the Devonian period but declined and fell into extinction by the end of the Permian period.
Cartilaginous Fish (Class Chondrichthyes)
Cartilaginous fish, better known as sharks, skates, and rays evolved during the Silurian period. Cartilaginous fish have skeletons composed of cartilage, not bone. They also differ from other fish in that they lack swim bladders and lungs.
Bony Fish (Class Osteichthyes)
Members of the Class Osteichthyes first arose during the late Silurian. The majority of modern fish belong to this group (note that some classification schemes recognize the Class Actnopterygii instead of Osteichthyes). Bony fish diverged into two groups, one that evolved into modern fish, the other that evolved into lungfish, lobe-finned fish, and fleshy-finned fish. The fleshy finned fish gave rise to the amphibians.
Amphibians (Class Amphibia)
Amphibians were the first vertebrates to venture out onto land. Early amphibians retained many fish-like characteristics but during the Carboniferous period amphibians diversified. They retained close ties to water though, producing fish-like eggs that lacked a hard protective coating and requiring moist environments to keep their skin damp. Additionally, amphibians underwent larval phases that were entirely aquatic and only the adult animals were able to tackle land habitats.
Reptiles (Class Reptilia)
Reptiles arose during the Carboniferous period and quickly took over as the dominant vertebrate of the land. Reptiles freed themselves from aquatic habitats where amphibians had not. Reptiles developed hard-shelled eggs that could be laid on dry land. They had dry skin made of scales that served as protection and helped retain moisture. Reptiles developed larger and more powerful legs than those of amphibians. The placement of the reptilian legs beneath the body (instead of at the side as in amphibians) enabled them greater mobility.
Birds (Class Aves)
Sometime during the early Jurassic, two groups of reptiles gained the ability to fly and one of these groups later gave rise to the birds. Birds developed a range of adaptations that enabled flight such as feathers, hollow bones, and warm-bloodedness.
Mammals (Class Mammalia)
Mammals, like birds, evolved from a reptilian ancestor. Mammals developed a four-chambered heart, hair covering, and most do not lay eggs and instead give birth to live young (the exception is the monotremes).
Progression of Vertebrate Evolution
The following table shows the progression of vertebrate evolution (organisms listed at the top of the table evolved earlier than those lower in the table).
|Animal Group||Key Features|
|Jawless Fish||- no jaws
- no paired fins
- gave rise to placoderms, cartilaginous and bony fish
|Placoderms||- no jaws
- armored fish
|Cartilaginous fish||- cartilage skeletons
- no swim bladder
- no lungs
- internal fertilization
|Bony fish||- gills
- swim bladder
- some developed fleshy fins (gave rise to amphibians)
|Amphibians||- first vertebrates to venture out onto land
- remained quite tied to aquatic habitats
- external fertilization
- eggs had no amnion or shell
- moist skin
- hard-shelled eggs
- stronger legs positioned directly beneath body
- hollow bones
- mammary glands


As part of our Harvest celebrations, we collected money for Christian Aid’s Harvest Appeal. We learnt about women in Malawi whose homes and farms had been destroyed by flooding and strong winds. We donated money to help them with their new business of making juice from the baobab.
We learnt that the storms and floods that affected Malawi were caused by climate change, and that climate change will continue to cause extreme weather all over the world.
Every child in our school made a prayer boat and we sent these images to Cop26 to show our support for climate change. Our prayers asked God to help the world leaders make changes to protect the world from climate change.


The high noon sun makes it tough to inspect the shrubs and plants in the butterfly park without squinting. Despite the harsh conditions, the Athalye family — Sandeep, his wife, son, and mother — arrive at the two-acre park for their regular visit, not just to have a leisurely time, but to survey the thriving Eden they helped establish inside Mumbai’s green lung: the roughly 3000-acre Aarey Milk Colony.
The Athalye family’s weekend trip to Aarey, adjacent to a congested highway in the heart of the city, is sacrosanct: they are continuing a deeply rooted tree-planting family tradition.
It all started in the year 2000 when Sandeep’s father, Vinay Athalye, who used to regularly take leisurely walks at Aarey, decided to plant around 100 saplings. Driven by passion and support from his fellow walkers and family, over the next 15 years, the elder Athalye managed to plant around 5500 saplings in Aarey.
But he didn't plant just any kind of tree saplings. Athalye purposefully planted five native tree species: peepal (sacred fig/Ficus religiosa), vad (banyan/Ficus benghalensis), amla (Indian gooseberry/Phyllanthus emblica), bael (Aegle marmelos) and ashoka (Saraca asoca) that are collectively called panchavati (grove of five trees). These tree species are believed to increase oxygen in the atmosphere. Through the years, he added 550 saplings to a patch of land that was later publicly known as Panchavati and grew into a densely wooded area. Though the land was later allotted to one of Mumbai’s defense forces and became restricted to the public, Athalye was known for this deed.
After his father’s demise in 2015, Sandeep’s family continued the tradition of greening the city and planted approximately 700 saplings of native species to date. To fulfill the senior Athalye’s wish, the family planted another 1200 shrubs, and converted a barren plot into a bustling butterfly garden. “We must give back to nature,” said 39-year-old Sandeep.
Green and open spaces are a rarity in this densely populated megacity of over 12.4 million.
Rashneh Pardiwala, ecologist and founder of the Centre for Environmental Research & Education (CERE), explained, “One of the areas of concern in India is the diminishing availability of green spaces in our major metros. Today, Mumbai, for example, has just 1.1 square metres of open space per person, including Sanjay Gandhi National Park, gardens, recreation grounds, and playgrounds.”
Mumbai falls short of World Health Organization’s (WHO) recommendation of nine square metre green open spaces by almost nine times. The 2017 tree census conducted by the governing civic body of Mumbai, Brihanmumbai Municipal Corporation (BMC) revealed that the city had one tree for every four people. Further, a recent study by Indian Institute of Technology (IIT)-Bombay highlighted how people from lower socio-economic backgrounds have less access to green space.
That’s not good news for the coastal city, which is vulnerable to the impacts of climate change.
Trees are beneficial to the environment. They act as a carbon sink by absorbing carbon dioxide (CO2) from the atmosphere and releasing oxygen (O2). An increase in atmospheric CO2 due to human activity is one of the leading causes of climate change. A recent study claims a natural solution to mitigate this: plant a trillion trees in specific regions of the world to capture 25% of carbon dioxide.
CERE has planted 46,557 large native trees across 61 locations including many sites in Mumbai under its Urban Afforestation Project (UAP). The initiative helps companies, organizations, and individuals reduce their carbon footprint and has sequestered 12,602 metric tonnes of CO2.
The benefits of planting tree canopy and green cover branch in several directions. It regulates the local climate and reduces the urban heat island effect caused by heat trapped between the artificial structures of the city. Urban trees also play a crucial role in reducing air pollution as the leaves absorb harmful pollutants from vehicles, industries, and other sources.
Green spaces in urban areas provide shelter to local biodiversity, act as sound and wind barriers, contribute to the social, mental, and physical well-being of people, and the list goes on.
Fortunately, the barren plot where the second-generation Athalyes have planted on are currently free for all to access and enjoy. A kaleidoscope of butterflies might welcome visitors who enter the butterfly garden. Aarey has recorded 86 butterflies species, and Sandeep is hopeful to shelter them here.
But his idea doesn’t end with planting a sapling. He invites volunteers to the plantation drives, shows them the benefits of planting indigenous trees and hopes to give them experience and get inspired. “They should not just become opportunities for selfies,” he said. “Once you plant a sapling, you have to monitor it like a baby for the first few years.”
A recent drive in a roughly three-acre plot of land that opened up to a view of Mumbai’s skyscrapers at the horizon pleasantly surprised Sandeep. He expected around 100 to 150 people to attend and plant 300 peepal saplings, but 400 - 500 people turned up.
Sandeep is relieved that his six-year-old son gets the chance to get his hands dirty in the soil. He believes it’ll help him form a bond with nature and become sensitive towards its issues.
India pledged to reduce its national emissions by creating an additional carbon sink for 2.5 to 3 billion tonnes of CO2 through additional forest and tree cover by 2030. Mumbai, like many other cities in India, bears witness to rapid urban infrastructure projects, mostly at a disproportionate environmental cost. At such a time, the efforts of individuals and communities to safeguard or revive existing ecosystems and create new ones can be crucial.


Kinross Gold says Chile mine operations still down after flooding
April 1 (Reuters) - Kinross Gold Corp said on Wednesday its mining and crushing operations at its Maricunga mine in northern Chile remain suspended following severe floods in the region last week.
Even so, the Toronto-based producer said it doesn't expect the temporary halt to affect its regional production forecast for the year. (Reporting by Nicole Mordant in Vancouver; Editing by Alan Crosby)
© Thomson Reuters 2017 All rights reserved.


Introduce yourself and your company
My name is Belén Linares, I am the Manager of Energy Innovation at Acciona and I have been working in the energy sector for over 20 years. I initially worked in the aerospace sector, because I am an aerospace engineer, but I was amazed by the energy sector very quickly.
My company is Acciona and I work in the energy division where I lead Innovation. Acciona is an electric company whose energy division has more than 11 GW installed worldwide of renewable technologies; it is a green utility: wind, photovoltaic, solar thermal, hydraulic and biomass.
What activities do you carry out in relation to energy storage?
Within the energy innovation portfolio, we work on several strategic programs, one of which is energy storage; we are obsessed with managing energy over time. In principle, we are working with electrochemical storage, or we are starting to bet on it, but we are also monitoring and observing thermal storage, and we are starting to be tremendously interested in stationary storage through gas storage.
What would you highlight about CIC energiGUNE?
Within Acciona´s innovation strategy, we work in an open innovation mode, which means that we monitor, accelerate, and follow all technology centers or partners that innovate and are excellent at doing what they do, such as CIC energiGUNE in the case of energy storage.
CIC energiGUNE is one of the technology centers in the world that we monitor from Acciona and in which we try to accelerate all those developments of very low TRL´s but that could have some application in the industry.
What is Acciona´s relationship with the Spin off Bcare?
We began our energy storage development by piloting two plants in Spain -our prototypes-, one with solar-photovoltaic and the other with wind power, and we chose CIC energiGUNE´s spin-off, Bcare, as the company to advise Acciona on the development of energy storage.
Now, we are going to invest in a utility-scale energy storage plant in Australia, in the Mortlake Park, with wind energy and Bcare is one of the technological agents that is advising us, for sizing, maintenance, construction and also for the suppliers of the value.


Forecasters warning of snow for Rotherham
IT’S time to wrap up this weekend with weathermen predicting Rotherham is in for a blast of cold weather . . . and even some snow.
The Met Office has issued a yellow warning of snowshowers for the region over the weekend and particularly on Monday, with accumulations of up to 5cm at low levels.
Forecasters also say icy surfaces will be a problem on Monday morning.


Who loves mosquitoes? Said no one, ever!!!
Mosquitoes are tiny creatures, flies which suck blood. The blood can be of humans, animals or any other creature, their bite causes a red bumps and a lot of irritation. Though the bite of a mosquito is harmless and there is no big loss of blood, they are scary because they can cause a lot of diseases. As they grow in unclean places and tend to bite all kinds of species, they are the biggest known vectors to a lot of diseases, like yellow fever, dengue, chickungunya and the latest being the zika virus. These diseases could be lethal for humans.
The backyard of the house is a place everybody wants to be at. Kids love to play there, elders generally stroll in the garden and that’s where most of our barbeque parties are. Nobody would ever want their backyard and then there house to be full of disease causing mosquitoes.
The best ways to get rid of mosquitoes or prevent them from entering your backyard would be-
As safety is always the first priority, one must never take mosquito growth in the surroundings lightly, as they can cause fatal diseases. One must always immediately contact a professional and on a day to day basis try and keep our surroundings clean and prevent them from growing.
Need a reputable, affordable and responsive pest control company in Albany? Give All Star Pest Solutions a call today!
All Star Pest Solutions is a certified pest control company offering a full suite of services to Albany and the surrounding Capital Region.


May 17, 2023
SEOUL – South Korea heightened its earthquake alert level by one notch from blue to yellow as a 4.5-magnitude quake was detected off the east coast Monday morning, affecting cities on the nation’s eastern coast.
The alert level is raised when at least three quakes are detected in a specific area within a short range of time, and calls for cooperation between several government bodies on the issue, Prime Minister Han Duck-soo’s office said in a statement.
Han urged the Interior Ministry to inspect the nation’s resilience against earthquakes. He also called on the industry, science and land ministers to monitor the operation of critical infrastructure for the power supply, telecommunications and traffic systems.
The upgrade came three weeks after the Interior Ministry had issued a blue alert — the lowest on the scale of four — on April 25. Since then, four quakes stronger than magnitude 3, including Sunday’s 3.1-magnitude quake, have taken place over the course of the last three weeks within a 50-kilometer radius of the epicenter.
It was the first alert level above blue issued since October, when a 4.1-magnitude quake in Goesan County, North Chungcheong Province, led to an orange alert — one level higher than yellow.
Monday’s magnitude 4.5 earthquake occurred at 6:27 a.m. at a depth of 31 kilometers in the East Sea, 52 kilometers off the coastal city of Donghae, Gangwon Province. It was this year’s strongest quake out of 44 with magnitude 2.0 or greater detected in Korea so far. A minor 1.8-magnitude aftershock followed at 8:06 a.m.
The quake was felt in Gangwon Province and North Gyeongsang Province with a shaking intensity of level three, meaning people standing inside buildings in the region felt weak but noticeable tremors, according to the Korea Meteorological Administration.
Magnitude measures an earthquake’s size at its epicenter, while shaking intensity measures the degree of shaking caused by a quake at a specific location other than the epicenter.
A tremor of weaker intensity was also felt in North Chungcheong Province, according to the authorities.
No injuries or deaths from Monday’s quake were expected, but there were at least 18 reports of the tremor having been felt in Gangwon Province, as well as three in North Gyeongsang Province as of 7:30 a.m.
Also, no alerts were declared at nuclear power plants along the east coast, with the nearest one being about 88 kilometers away from the epicenter, according to the Nuclear Safety and Security Commission under Han’s office.
It was the 22nd-strongest quake ever detected on or around the Korean Peninsula.
Also, it was the strongest quake by magnitude out of the 42 quakes reported in the area since Korea began recording earthquakes in 1978.
According to the KMA’s estimate, Monday’s quake might have come from an unidentified reverse fault on the East Sea.
Experts say Monday’s quake might have to do with a nearby Hupo fault, unlike a series of small earthquakes in the area coming from an unidentified right-lateral fault, the movement of which is in the right-hand direction.
“This means we are not sure if the main shock in the area has already come or not,” said Lee Hee-kwon, geology professor at Kangwon National University.
Work to identify the faults in ocean areas off the Korean Peninsula — including the quake-hit area — have been underway in a pangovernmental research project for the past couple of decades, but it is unlikely that Seoul will be able to confirm where the active faults in the area are in the near future, a KMA official told The Korea Herald on Monday.
Korea experienced its strongest earthquake with the magnitude of 5.9 in 2016 in Gyeongju, North Gyeongsang Province, causing damage to historical sites and artifacts as well as disrupting telecommunications operations.
It was followed by a 5.4-magnitude quake in Pohang, North Gyeongsang Province, in 2017, which left over 90 injured and displaced nearly 1,800.


- Reclaimed Paneling & Flooring
This project incorporated 3,100 square feet of reclaimed wood paneling and flooring milled from dense hardwoods from tropical lowlands.
Nestled in the historic neighborhood of Mission Hills, The Patio on Goldfinch is a gathering place focused on relaxation, enjoyment and sharing. It features a living green wall, Torrey Pines table, outdoor fireplace and pet-friendly patio. Everyone is naturally drawn to the huge, lush plant wall in the back of the room, designed by The Patio's on-staff decorator. Another plant wall appears on the al fresco patio.
The project owners are aiming LEED Platinum certification for the restaurant and anticipate achieving at least LEED Gold.
This project was coordinated by our Global Reuse Services team while working as an independent company, PlanetReuse.


The Science Media Centre recently held a briefing for journalists on the recent slowdown in global surface temperature rise, and published an accompanying briefing note. The Met Office also released three reports on the topic.
The key points were: (1) recent changes need to be put in longer term context & other climate indicators such as sea level, Arctic sea ice, snow cover, glacier melt etc are also important; (2) the explanation for recent slowdown is partly additional ocean heat uptake & partly negative trends in natural radiative forcing (due to solar changes and small volcanic eruptions) which slightly counteract the positive forcing from GHGs; (3) the quantification of the relative magnitude of these causes is still work in progress; (4) climate models simulate similar pauses.
There were several articles in the media following the briefing, including BBC, The Telegraph, The Independent, amongst others. One theme in the articles was that the possible existence of such pauses were a surprise to journalists, but not to the scientists. As the observations of global temperature have always shown such variability it should not have been too surprising, but perhaps this message was not expressed or communicated clearly to the media before? Or perhaps it just was not as interesting to the media before?
Early discussions on role of variability
Previous IPCC reports have certainly highlighted the role of variability – for example:
- FAR 1990: Simulated decadal variability was present in some of the first climate simulations (Figure 6.2). Also discussed in the Executive Summary of the SPM.
- TAR 2001: Section 220.127.116.11 – Signal versus noise, including Figures 9.2 and 9.3
One of the earliest coordinated efforts which considered the role of variability in projections was the EU PREDICATE project which ran from 2000 to 2003. Figure 1 below (published in CLIVAR Exchanges #19 in 2001) shows observations and three projections of European temperatures from 1996 to 2020 with different realisations of the variability. This type of work helped motivate the use of initial condition information to make experimental decadal climate predictions.
The communication of the role of variability has increased markedly since the IPCC AR4 (which was unfortunately a little quieter on this topic), at least partly due to the recent slowdown. Recent articles, such as that of Deser et al., have helped this communication.
Two figures used at the briefing also appear below. The first highlights that simulations do show similar pauses to those seen in recent observations, and the second, which appears in the briefing note, will be familiar to regular readers of Climate Lab Book. It compares the observed global temperatures with the CMIP5 simulations, but with a longer term context than previous versions.
Overall, the important role of variability in near-term climate has been known since the early climate simulations, but perhaps not communicated as widely as it might have been.


Thu, 22 December 2022
For years now marine biologists, conservationists, NGOs, and governments have been working to protect the oceans in places all over the world. It would make sense that the projects would be about working altogether, especially with the people that live along the coastal community. Unfortunately, that hasn’t always happened. Coastal communities have been ignored during these projects. Luckily, there seem to be more conversations around Justice and equity with environmental projects, which could lead to a better future. I am going to talk about ocean Justice and Equity in this episode.
Fill out our listener survey: https://www.speakupforblue.com/survey


Rain can do a lot of things, but the last thing that it does is to make your windows dirty. Although it can potentially make your window appear dirtier, it’s only because the dirt was already there, to begin with.
Most homeowners often rely on a professional commercial window cleaning service such as Utah Window Cleaning Inc in Sandy to do the job. However, it’s still important to know if most window cleaning myths are true or not. So, does rainwater really make your windows dirty?
Rain actually keeps your window cleaner for a longer period. However, it’s easy to confuse groundwater and rainwater. The truth is, groundwater is a reason your windows are dirty. Groundwater often contains various chemicals and mineral deposits that aren’t commonly found in rainwater.
These chemicals and other particulates tend to stay behind the mirror once the water evaporates, which then leaves white spots where the water previously evaporated. However, rainwater doesn’t have any mineral deposits in it, which in turn doesn’t leave any spot once it dries.
Rainwater as Window Cleaners
Rainwater tends to keep levels of specific matter much lower. So if you keep your windows free from dirt or grime, then the rainwater will help keep it protected from any dirt or small debris.
Air also has an effect on your windows as well. If the air is free from dust, then there’s a less chance that dirt will settle on the glass surface once the raindrop hits your windows.
In general, rainwater doesn’t constitute to dirty. However, you still have to regularly keep it clean at all times to prevent any dirt from accumulating. You might want to hire a professional window cleaning service to do the job for you.


Fish Passage Through a Simulated Horizontal Bulb Turbine Pressure Regime: A Supplement to"Laboratory Studies of the Effects of Pressure and Dissolved Gas Supersaturation on Turbine-Passed Fish"
Description: Migratory and resident fish in the Columbia River Basin are exposed to stresses associated with hydroelectric power production, including pressure changes during turbine passage. The responses of fall chinook salmon and bluegill sunfish to rapid pressure change was investigated at the Pacific Northwest National Laboratory. Previous test series evaluated the effects of passage through a vertical Kaplan turbine under the"worst case" pressure conditions and under less severe conditions where press… more
Date: July 31, 2003
Creator: Abernethy, Cary S.; Amidan, Brett G. & Cada, G. F.
Partner: UNT Libraries Government Documents Department


PUCO approves AEP-Ohio energy efficiency plan
COLUMBUS, OHIO (Jan. 18, 2017) – Today the Public Utilities Commission of Ohio (PUCO) adopted an agreement to allow AEP-Ohio to continue and expand its energy efficiency and peak demand reduction programs through 2020.
AEP-Ohio will offer a variety of programs to assist residential and business customers in reducing their energy demand. Programs include efficient product rebates and incentives, appliance recycling programs, in-home energy audits, home weatherization assistance, and various educational and training programs.
The Commission also adopted a $110 million annual program cost cap to protect against potential increases in the cost of energy efficiency and peak demand reduction programs.
On Dec. 9, 2016 AEP-Ohio and 13 interested parties signed a stipulation resolving all outstanding issues and recommended Commission approval. The stipulation was signed by AEP-Ohio, PUCO staff, Environmental Defense Fund, Environmental Law & Policy Center, Interstate Gas Supply, Inc., Kroger, Mid-Ohio Regional Planning Commission, Natural Resources Defense Council, Ohio Energy Group, Ohio Environmental Council, Ohio Farm Bureau Federation, Ohio Hospital Association, Ohio Manufacturers’ Association and Ohio Partners for Affordable Energy.
The PUCO is responsible for evaluating electric distribution utilities’ energy efficiency and peak demand portfolio programs, which must be cost-effective in reducing energy usage or peak demand consistent with state policy. Utilities are required by law to meet certain energy-reducing benchmarks annually.
The Public Utilities Commission of Ohio (PUCO) is the sole agency charged with regulating public utility service. The role of the PUCO is to assure all residential, business and industrial consumers have access to adequate, safe and reliable utility services at fair prices while facilitating an environment that provides competitive choices. Consumers with utility-related questions or concerns can call the PUCO Call Center at (800) 686-PUCO (7826) and speak with a representative.
Subscribe and unsubscribe to the PUCO Media Release email service


Canada’s adoption of renewable power sources: NE2-17/2-2017E-PDF
energy market analysis, May 2017.
“Canada generates a large share of its electricity from renewable sources, including hydro generation, compared to other countries. In 2015, renewables accounted for 66% of generation, up from 60% in 2005. Only five countries produce a similar or larger share of electricity from renewable sources: Norway, New Zealand, Brazil, Austria, and Denmark. In 2015, Canada was a global leader in total hydroelectricity production, second only to China. Canada is also a major producer of wind power; Canadian generation from solar and biomass sources is more modest. China, the United States, and Germany lead the world in total wind, solar, and biomass production. Many factors influence the adoption of different power sources, including installation and generation costs, reliability, and environmental considerations. Collectively, renewables such as wind and solar are becoming more cost competitive. The low carbon emissions associated with renewables have also aligned them with current policy priorities. As a result, the increased adoption of renewables is expected to continue in Canada and abroad"--Executive summary, p. 1.
|Department/Agency||National Energy Board.|
|Title||Canada’s adoption of renewable power sources|
|Subtitle||energy market analysis, May 2017.|
|Series Title||Energy market analysis ;|
|Publication Type||Series - View Master Record|
|Other Language Editions||[French]|
|Electronic Document|| |
Information identified as archived is provided for reference, research or recordkeeping purposes. It is not subject to the Government of Canada Web Standards and has not been altered or updated since it was archived. Please contact the authoring department to request a format other than those available.
We invite you to consult the Frequently Asked Questions page for additional information regarding the Archived Content notice.
Note: The URLs contained in this/these document(s) may no longer be functional
|Note||Issued also in French under title: Adoption des sources d’énergie renouvelable au Canada : analyse des marchés de l’énergie, mai 2017. Cover title.|
|Number of Pages||ii, 28 p. :|
|Subject Terms||Renewable energy, Market analysis|
- Date modified:


With food costs ever-rising, and with each of us taking more care about choosing responsibly in the supermarket, it can be a challenge to find nutritious, high protein foods that are also sustainable.
Saldanha Bay is a large, sheltered bay on the Cape west coast about a 90 minute drive north of Cape Town. Farming in the sea, also known as aquaculture, is the practice of rearing water creatures for food and is different from fishing or foraging in that it is practiced with a strong focus on renewing the farming resource, and protecting the environment that supports the aqua farm.
Mussels and Oysters are known as bivalve molluscs, or shellfish that have two opposing shells that open and close to allow the creature inside to eat and breathe. That creature on the inside is the meaty, delicious bit that we eat – either cooked or raw – which is high in omega 3’s, protein, and other minerals such as B12 and selenium.
Although mussels and oysters are cousins, they have very different life cycles.
In South African ocean waters you will find two species of mussels – the Mediterranean mussel (with the familiar orange or white flesh) and the South African mussels, that has a darker, brown flesh. Both are just as delicious, but the South African mussels sometimes requires a few minutes extra cooking time. Mussels will grow spontaneously on almost any surface – I’m sure you’ve seen them growing on rocks and jetties? At the mussel farm, floating rafts are anchored in the bay, with 6m lines of rope hanging down into the water. All that is needed to collect the mussels, is to pull of the rope, strip off the mussels, rinse them with sea water and send them back to the factory for packaging. Mussels reach the ideal size, about 8cm, within one year and are ready to be harvested.
The oysters, predictably, need a little more care and nurture. Oysters are grown from ‘seed’ which are tiny, baby oysters about the size of a grain of rice. The oyster babies are put into cages, that are submerged about 2m under the water, anchored to a long line. At various intervals of 45 to 60 days, the cages are raised for inspection and to check on the little oyster babas. As they grow, they are graded by size, and reloaded into enclosures that are larger and larger.
A medium size oyster that you might expect to have served to you in a restaurant, would have been in the ocean growing for about 10-12 months. Extra large oysters are farmed for up to 18 months.
At the Saldanha Bay farms, no products are added to water. Only the infrastructure, such as rafts and ropes are installed, to provide the ideal environment for the molluscs to naturally make their home. For both the mussels and the oysters, all that they need to grow and reproduce, is available right there in the water. Each tide brings in fresh waters from the ocean, filled with nutrients and oxygen, keeping the molluscs happy!
Storing, cooking and serving
Oysters and mussels are harvested early every morning, depending on the weather and sea conditions. Freshness is key here – but did you know that live mussels and oysters can be refrigerated for up to 4 days? Sea water samples are tested on a regular basis to check for algae blooms, also known as red tides. Algal bloom does not harm the molluscs, but the toxins present in the flesh of themussel and oyster are not safe for human consumption.
Oysters are of course, best served chilled and raw. The flesh is a medium texture that is smooth, and has a salty taste like fresh sea water, with a sweeter flavour at the end. Enjoy oysters however you prefer – they are excellent with fresh lemon, black pepper and maybe a little bit of chilli. They are also delicious with a sweet and spicy vinaigrette dressing and best followed by ice cold sparkling wine or beer!
If you have bought live mussels – that is the fresh, closed shells – then the easiest way to prepare them is to steam them with a little flavoured liquid, butter and fresh herbs. We love them with garlic, butter and parsley and served with chunks of baguette to catch all the delicious sauce.
A special thank you for hosting us in Saldanha Bay to Wild Peacock Products, the exclusive supplier to the restaurant industry of products from Blue Ocean Mussels and Saldanha Bay Oyster Company.
You can order live mussels, delivered straight to your home!


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“This is in no way indicating the end of the world,” said Eric Smith, astronomy instructor at Southern Polytechnic State University in Marietta. “It’s a random one-off event.”
That said, Smith said the event was quite surprising.
Experts say smaller meteorites hit the earth five to 10 times a year, but strikes the size of the one over the Ural Mountains only happen between every five and 10 years.
“Meteorites fall all the time on earth, but most of them are very, very small,” said Nikolaos Kidonakis, associate professor of physics at Kennesaw State University.
And, usually, those hit uninhabited areas.
“For as spread out as we are today, the unpopulated areas are still more than the populated areas,” Smith said. “Just by sheer numbers, you’re more likely to get a hit in an unpopulated area than a populated area.”
The strike reminded Smith of another impact in Russia. In 1908, a comet exploded over Siberia that caused the largest recorded impact in history. Though the “Tunguska event” was much larger, it didn’t injure anyone.
Making Friday’s event more unusual was that the meteorite struck on the same day that an asteroid passed within 17,150 miles of earth, the closest known flyby of a rock that size.
“I don’t know if it is just a coincidence or if a piece (of the asteroid) got broken off, or maybe the asteroid was tailing some smaller rocks,” Smith said.
But Kidonakis said the two aren’t necessarily related.
“Meteorites are very common,” he said. “Asteroids coming by the earth is more rare.”
European Space Agency spokesman Bernhard Von Weyhe said Asteroid 2012DA14 is unrelated to the meteorite strike in Russia.
Meteorites have even struck in north Georgia. A 10-ounce meteorite that tore a hole in the roof of a home in Cartersville in 2009 is now on display at the Tellus Science Museum there. It was the 25th found in the state.
Michael Peck with the U.S. Geological Survey’s office in Atlanta said other meteorites were formerly on display at the state Capitol, but have been moved to Tellus.
Alan Harris, a senior scientist at the German Aerospace Center in Berlin, said most of the damage in Russia on Friday would have been caused by the explosion of the meteor as it broke up in the atmosphere. The explosion caused a shockwave that sent windows and loose objects flying through the air in a radius of several kilometers. By the time the remaining fragments hit the ground, they would have been too small to cause significant damage far from the site of impact, he said.
Von Weyhe said experts from Europe, the United States and Russia are already discussing how to spot potential threats sooner and avert them, particularly in case there is danger of a meteorite hitting a major city. But don’t expect a Hollywood style mission to fly a nuclear bomb into space and blow up the asteroid.
“It’s a global challenge and we need to find a solution together,” he said. “But one thing’s for sure, the Bruce Willis ‘Armageddon’ method won’t work.”
— The Associated Press contributed to this story


On the limit to the accuracy of regional-scale air quality models
Regional-scale air pollution models are routinely being used worldwide for research, forecasting air quality, and regulatory purposes. It is well recognized that there are both reducible (systematic) and irreducible (unsystematic) errors in the meteorology–atmospheric-chemistry modeling systems. The inherent (random) uncertainty stems from our inability to properly characterize stochastic variations in atmospheric dynamics and chemistry and from the incommensurability associated with comparisons of the volume-averaged model estimates with point measurements. Because stochastic variations are not being explicitly simulated in the current generation of regional-scale meteorology–air quality models, one should expect to find differences between the model estimates and corresponding observations. This paper presents an observation-based methodology to determine the expected errors from current-generation regional air quality models even when the model design, physics, chemistry, and numerical analysis, as well as its input data, were “perfect”. To this end, the short-term synoptic-scale fluctuations embedded in the daily maximum 8 h ozone time series are separated from the longer-term forcing using a simple recursive moving average filter. The inherent uncertainty attributable to the stochastic nature of the atmosphere is determined based on 30+ years of historical ozone time series data measured at various monitoring sites in the contiguous United States (CONUS). The results reveal that the expected root mean square error (RMSE) at the median and 95th percentile is about 2 and 5 ppb, respectively, even for perfect air quality models driven with perfect input data. Quantitative estimation of the limit to the model's accuracy will help in objectively assessing the current state of the science in regional air pollution models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to ambient measurements.
Confidence in model estimates of pollutant distributions is established through direct comparisons of modeled concentrations with corresponding observations made at discrete locations for retrospective cases. Pinder et al. (2008) discussed the reducible (i.e., structural and parametric) uncertainties that are attributable to the errors in model input data (e.g., meteorology, emissions, and initial and boundary conditions) as well as our incomplete or inadequate understanding of the relevant atmospheric processes (e.g., chemical transformation, planetary boundary layer evolution, transport and dispersion, deposition, rain, and clouds). Inherent or irreducible (random or unsystematic) uncertainties stem from our inability to properly characterize the stochastic nature of the atmosphere (Wilmott, 1981; Wilmott et al., 1985; Fox, 1984; Rao et al., 1985, 2011a, b; Dennis et al., 2010) and from the incommensurability associated with comparing the volume-averaged model estimates with point measurements (e.g., McNair et al., 1996; Swall and Foley, 2009). Also, without completely knowing the three-dimensional initial physical and chemical state of the atmosphere, its future state cannot be simulated accurately (Lamb, 1984; Lamb and Hati, 1987; Lewellen and Sykes, 1989; Pielke, 1998; Gilliam et al., 2015). Given the presence of the irreducible uncertainties, precise replication of observed concentrations or their changes by the models cannot be expected (Dennis et al., 2010; Rao et al., 2011a; Porter et al., 2015; Astitha et al., 2017).
Whereas an air quality model's prediction represents some time-/space-averaged concentrations, an observation at any given time at a monitoring location reflects an individual event or specific realization out of a population that will almost always differ from the model estimate even if the model and its input data were perfect (Rao et al., 1985). Consequently, comparisons of modeled and observed concentrations paired in space and time indicate biases and errors in simulating absolute levels of pollutant concentrations at individual monitoring sites (Porter et al., 2015). The scientific discussion on modeling uncertainty goes back more than 3 decades with the current practice including data assimilation, ensemble modeling, and model performance evaluation (e.g., Fox, 1981, 1984; Lamb, 1984; Demerjian, 1985; Oreskes et al., 1994; Pielke, 1998; Lewellen and Sykes, 1989; Lee et al., 1997; Carmichael et al., 2008; Hogrefe et al., 2001a, b; Biswas and Rao, 2001; Grell and Baklanov, 2011; Gilliam et al., 2006; Herwehe et al., 2011; Baklanov et al., 2014; Bocquet et al., 2015; Solazzo and Galmarini, 2015a; Ying and Zhang, 2018; McNider and Pour-Biazar, 2020; Stockwell et al., 2020). While ever-improving process knowledge and increasing computational power will continue to help reduce the structural and parametric uncertainties in air quality models, the inherent uncertainty associated with our inability to properly characterize the stochastic nature of the atmosphere will always result in some mismatch between the model results and measurements; this could lead to speculation on the inferred accuracy of the future states simulated by the regional-scale air quality models (Dennis et al., 2010; Rao et al., 2011a; Porter et al., 2015; Astitha et al., 2017; Luo et al., 2019).
The sensitivity of model results to meteorology, chemical mechanisms, and emissions has been examined in numerous studies (e.g., Vautard et al., 2012; Sarwar et al., 2013; Pierce et al., 2010; Napelenok et al., 2011; Kang et al., 2013). Herwehe et al. (2011) attributed the differences in ground-level ozone predictions between the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem) and the modeling system consisting of WRF and the Community Multiscale Air Quality (CMAQ) model (WRF-QMAC) to the way meteorology and chemistry interactions are handled within these two modeling systems. Thomas et al. (2019) examined the ozone predictions in the mid-Atlantic region of the United States during June 2016 through a series of simulations with WRF-Chem, focusing on the sensitivity to the meteorological initial/boundary conditions (IC/BCs), emissions inventory (EI), and planetary boundary layer (PBL) scheme. Ying and Zhang (2018) discussed the use of satellite-based observations for improving the predictability of multiscale tropical weather and equatorial waves. Ensemble modeling is being advocated for quantifying the uncertainty in model predictions; however, the spread in the model estimates for the variable of interest reflects the impact of our incomplete or inadequate knowledge of the physical and chemical processes (i.e., the reducible errors stemming from structural and parametric uncertainty) occurring in the atmosphere (Solazzo and Galmarini, 2015b; Thomas et al., 2019; Stockwell et al., 2020). McNider and Pour-Biazar (2020) reviewed the many issues in predicting the prevailing meteorology for regional air quality simulations and indicated that errors in the specification of the physical atmosphere such as temperature, winds, and mixing heights can affect the air quality predictions. Stockwell et al. (2020) discussed the problems relating to the atmospheric chemical mechanisms currently being used for simulating air quality. The current generation of regional models consider only the mean values of a meteorological variable for a given timescale and the average rate constant derived from gas chamber experiments for chemical reactions and does not include their fluctuations in solving the equations of motion for each time step. Further, the current operational regional-scale meteorological and air quality models do not explicitly simulate the stochastic nature of the atmosphere and, as such, typically miss the extreme values at both the low and high ends of the concentration distribution function.
In most applications of regional-scale air quality models, statistical metrics such as bias, the root mean square error (RMSE), correlation, and the index of agreement are used to judge the quality of model predictions and determine if the model is suitable for forecasting or regulatory purposes (e.g., Fox, 1981, 1984; Solazzo et al., 2012; Appel et al., 2012; Simon et al., 2012; Foley et al., 2014; Ryan, 2016; Emery et al., 2016; Zhang et al., 2016; U.S. Environmental Protection Agency, 2018). While significant improvements in the formulation, physical and chemical parameterizations, and numerical techniques have been implemented in atmospheric models over the past 3 decades, it is not clear if the improvement claimed in the model's performance relative to the routine network measurements is statistically significant based on these metrics (Hogrefe et al., 2008). Also, no assessments have been made to date on the errors that are to be expected even from “perfect” regional-scale air quality modeling systems. To estimate such irreducible model errors due to atmospheric stochasticity (which we consider to be the errors that are expected even from a perfect model – devoid of structural and parametric uncertainties – with perfect – error-free – inputs), we analyzed the observed daily maximum 8 h (DM8HR) ozone time series data at monitoring locations across the contiguous United States (CONUS) during the 1981–2014 time period and present the results of this analysis in Sect. 3.1. In Sect. 3.2, we illustrate how this information could be used in guiding model development specifically aimed at addressing reducible errors in the synoptic (SY) component by contrasting the results from Sect. 3.1 with analysis using the synoptic component from a 21-year simulation performed with the fully coupled WRF-CMAQ simulations covering the 1990–2010 period. Since we relied on multi-decadal historical ozone observations to assess the impact of the stochastic nature of the atmosphere, the results presented here are applicable to both forecasting and retrospective applications of current regional-scale air quality models.
Ground-level DM8HR ozone data covering CONUS during May to September in each year were obtained from the U.S. Environmental Protection Agency's (EPA) Air Quality System (AQS) (see https://www.epa.gov/aqs, last access: 3 February 2020). A valid ozone season consists of at least 80 % data coverage during May to September at each station. A total 185 monitoring stations with at least 30 valid years (to provide enough variety of synoptic conditions, denoted hereafter as 30+ in this paper) from the year 1981 to 2014 are analyzed. Also, fully coupled WRF-CMAQ model simulations over CONUS for the 1990–2010 period were utilized in this study to demonstrate a new perspective on model performance evaluation. To ensure better characterization of the prevailing meteorology (i.e., synoptic forcing) in the retrospective 21-year WRF-CMAQ simulations, four-dimensional data assimilation (FDDA) was utilized following the methodology suggested by Gilliam et al. (2012) and modified for fully coupled meteorology–chemistry model applications as described in Hogrefe et al. (2015). The model setup and performance evaluation of these historical multiyear WRF-CMAQ simulations have been published by Xing et al. (2015), Gan et al. (2015), and Astitha et al. (2017). Time-varying chemical lateral boundary conditions are nested from the 108 km hemispheric WRF-CMAQ simulation from 1990 to 2010 (Xing et al., 2015).
It has been shown that time series of the daily maximum 8 h ozone concentrations contain fluctuations operating on different timescales (e.g., intra-day forcing induced by the fast-changing emissions and atmospheric boundary layer evolution; diurnal forcing induced by the day and night differences; and synoptic forcing induced by the passage of weather systems across the country, sub-seasonal forcing due to the Madden–Julian Oscillation – MJO, and long-term forcing induced by emissions, El Niño–Southern Oscillation – ENSO, climate change, and other slow-varying processes such as seasonal and sub-seasonal variations in the atmospheric deposition and stratosphere–troposphere exchange processes) as noted by Rao et al. (1997), Vukovich (1997), Hogrefe et al. (2000), Porter et al. (2015), Astitha et al. (2017), Xing et al. (2016), and Mathur et al. (2017). Variations in the 8 h ozone can be thought of comprising of the baseline (BL) of pollution that is created by various emitting sources and modulated by the prevailing synoptic weather conditions (Rao et al., 1996, 2011b). Thus, the magnitude of the baseline concentration and the strength of the synoptic component should be viewed as the necessary and sufficient conditions for how high ozone levels can reach on a given day (Astitha et al., 2017). Scale separation can be achieved by applying filtering methods such as the empirical mode decomposition (EMD; Huang et al., 1998), elliptic filter (Poularika, 1998), Kolmogorov–Zurbenko (KZ) filter (Rao and Zurbenko, 1994), adaptive filter technique (Zurbenko et al., 1996), and wavelet (Lau and Weng, 1995). Because improved complete ensemble empirical mode decomposition with adaptive noise (Improved CEEMDAN; Colominas et al., 2014; a version of the empirical mode decomposition method) and KZ filter yielded similar results for the DM8HR time series data as shown in Figs. 1–2 discussed in the next section, only the results from the KZ filter are presented in the subsequent analysis for quantifying the impact of the stochastic nature of the atmosphere on observed and simulated ozone concentrations. Furthermore, the KZ filtering is a simple method and works well even in the presence of missing data (Hogrefe et al., 2003). In this study, we used the KZ5,5 with a window size of 5 d and five iterations on raw ozone time series [O3 (t)] in the same manner as in Luo et al. (2019), Porter et al. (2015), and Rao et al. (2011b). The size of the window and the number of iterations determine the desired scale separation. The KZ5,5 filtering process helps separate the synoptic-scale weather-induced variations embedded in the May–September DM8HR time series data (short-term component) from the long-term baseline component.
Because we are working with the daily maximum 8 h ozone data, the Nyquist interval is 2 d, indicating that the dynamical features having timescales less than 2 d (e.g., intra-day forcing from fast changing emissions and chemical transformations, boundary layer evolution, and diurnal forcing due to night vs. day differences) are not resolvable in this analysis (see Fig. 2 in Dennis et al., 2010). The 50 % cutoff frequency for the KZ5,5 is ∼24 d, and, hence, timescales less than those associated with large-scale weather fluctuations are embedded in the short-term or SY forcing. The KZ filtering is applied to both DM8HR observations and modeled DM8HR time series. Once the baseline is separated from the original DM8HR time series from all monitoring stations, then the synoptic forcing in the historical ozone time series data is used to estimate the variability in ozone concentrations that can be expected because of the chaotic/stochastic nature of the atmosphere by taking into account the relationship between the strength of synoptic forcing and mean of baseline ozone at each location over CONUS; this methodology was applied to both measured and modeled ozone concentrations (see details in Luo et al., 2019). Whereas the focus of Luo et al. (2019) was on transforming the deterministic modeling results into a probabilistic framework for assessing the efficacy of different emission control strategies in achieving compliance with the ozone standard, this paper is aimed at quantifying the model performance errors to be expected at each monitoring site over CONUS even from perfect regional-scale ozone models driven with perfect input data from the ever-present stochastic nature of the atmosphere.
3.1 Analysis of ambient ozone data
Using both Improved CEEMDAN and KZ filtering methods, we separated the synoptic forcing (timescale <24 d) and baseline (timescale >1 month) forcing embedded in the time series of observed and modeled daily maximum 8 h ozone concentrations. To illustrate, the results from the application of Improved CEEMDAN to the daily maximum 8 h ozone time series data measured at Altoona, PA, are presented in Fig. 1. The top left panel displays the raw ozone time series, while the top of the right panel shows its power spectrum. The seven intrinsic mode functions (IMFs) and the residual on the left side as well as their corresponding power spectra on the right reveal that most of the synoptic-scale features in ozone data are imbedded in IMFs 1 and 2. The baseline ozone is extracted by removing the first two IMFs from the raw ozone time series. To illustrate the concept of the ozone baseline, DM8HR time series measured in 2010 at Altoona, PA, are presented in Fig. 2a together with the embedded baseline concentration as extracted by the KZ5,5 and Improved CEEMDAN. It is evident that high ozone levels are always associated with the elevated baseline. The difference between the raw ozone time series and baseline, denoted as the short-term or synoptic forcing, is displayed in Fig. 2b. The power spectra, displayed in Fig. 2c and d, reveal both methods yielded good scale separation. Due to the good agreement between both scale separation techniques, only the results from the KZ filter are presented for the remainder of the paper.
Once the scale separation is achieved with the KZ5,5, we superimposed the SY forcing imbedded in 30+ years of historical DM8HR ozone time series measured at a given location on the baseline component of the ozone time series at that location to generate 30+ reconstructed or pseudo ozone distributions. Illustrative results using Eq. (3) at a suburban location in Altoona, PA, are presented for the 2010 base year in Fig. 3a; it should be noted that the linear relationship between the strength of SY (defined as the standard deviation of the data in the synoptic component) and the magnitude of the BL (defined as the mean of the data in the baseline component) has been taken into account in generating 30+ years of adjusted SY forcing as illustrated in Luo et al. (2019). As expected, there is excellent agreement between the average of 30+ values (solid blue line) and observed ozone in 2010 at each percentile of the concentration distribution function (red line). Also, the original cumulative distribution function (CDF) in 2010 (red line) is constrained within the 30+ CDFs of pseudo distributions (Fig. 3a); note that it is equally likely for any of these 30+ CDFs to occur because of the stochastic nature of the atmosphere even though the individual event in 2010 yielded the CDF shown in red. As mentioned before, an ozone mixing ratio at any given probability point on the red line in Fig. 3a reflects an individual event, while ozone values at the same probability in different CDFs (gray lines) reflect the population stemming from the stochastic nature of the atmosphere. In other words, there are 30+ dynamically consistent ozone time series attributable to the 2010 baseline (given 2010 emissions) for examining the inherent variability due to atmospheric stochasticity. It is evident in Fig. 3a that there is larger variability at the lower and upper percentiles than that in interquartile range, revealing that the tails of the concentration distribution function are subject to large inherent uncertainty. Using these 30+ pseudo-observation ozone mixing ratios and the actual observed ozone values at each percentile, statistical metrics such as bias, the RMSE, the coefficient of variation (), the normalized mean error (NME), and the normalized mean bias (NMB) are presented in Fig. 3b and c (see Emery et al., 2016, for the description of the statistical metrics considered here). As expected, the lower and upper tails of the distribution are prone to large errors. These results demonstrate the presence of substantial natural variability at the upper 95th percentile, which is of primary interest in regulatory analyses. The extreme values are better described in statistical terms rather than in deterministic sense (Rao and Visalli, 1981; Hogrefe and Rao, 2001; Luo et al., 2019).
Ozone time series at 185 monitoring stations covering CONUS, having at least 80 % data completeness, are analyzed in the above manner, and the results are displayed as box plots in Fig. 4. Note the presence of large variability in the CV, NME, NMB, and bias at the lower and upper percentiles (Fig. 4). The RMSE expected for the ozone mixing ratios in the interquartile range is ∼1.5 ppb, but it is >5 ppb for the upper 95th percentile (Fig. 4b). The spatial distribution of the RMSE at the 50th and 95th percentiles is displayed in Fig. 5a and b, respectively. The RMSE at the upper 95th percentile is very high at some monitoring sites in California and Michigan (Fig. 5b). Monitoring stations situated in the urban areas, near large bodies of water, and in regions of complex terrain influenced predominantly by local conditions tend to exhibit higher RMSE values. The elevation of the monitoring sites is displayed in Fig. 5c.
3.2 Analysis of modeled ozone concentrations
The analysis in the previous section quantified the inherent stochastic variability that is present in the SY component using long-term records of ozone observations. In this section, we analyze long-term records of model simulations in an attempt to quantify the error associated with the modeled SY component that results both from not explicitly representing stochastic variations in atmospheric dynamics in the current generation regional air quality models and from other reducible sources of model error. The model simulations were performed with the fully coupled WRF-CMAQ system with a 36 km horizontal grid cell size and covered the 21-year period from 1990 to 2010 (Gan et al., 2015). In this section, we examine the impact of superimposing different SY forcings embedded in ozone observations vs. those in the WRF-CMAQ model on the observed baseline concentration. To provide an illustration of the differences between observed and modeled time series over this period, Fig. 6a displays a scatter plot of the strength of the SY component (standard deviation of data in the SY component) vs. the mean of the baseline component for both observations and model simulations at the Altoona, PA, site. While both observations and WRF-CMAQ simulations show a strong correlation between these two variables, it is evident that at this monitoring location the standard deviation (i.e., strength) of the SY component is substantially lower for the WRF-CMAQ simulations for a given mean of the BL component (i.e., for any given year). The year-to-year variation in the observed and modeled mean of the BL and strength of SY forcing, displayed in Fig. 6b, reveals that the model overestimated the BL and underestimated the strength of SY forcing. The 36 km grid may be better for representing the large-scale synoptic forcing associated with the translation of weather systems than the meso-scale weather and urban influences (both dynamics and chemistry) that are embedded in the observed SY component. Meteorological modeling with higher horizontal grid resolution might be able to capture the land–sea breeze, lake–sea breeze, and terrain influences that observations are seeing at certain monitoring locations.
To isolate the impact of model imperfections on only the SY timescale on errors across the ozone distribution, we assume that the model perfectly reproduces the “true” BL depicted by the observed 2010 BL. We then use this perfect modeled BL and reconstruct “pseudo-simulated” ozone time series, like what was done in Fig. 3, except for using the SY component embedded in the 21 years of coupled WRF-CMAQ simulations. The rationale for this analysis is to quantify the amount of model error present in the current simulations that could conceivably be reduced through improving the representation of synoptic and mesoscale processes and/or increased horizontal resolution with appropriate data assimilation techniques. Figure 7a displays the CDF of actual observed ozone (red line) overlaid on 21 pseudo-simulated ozone CDFs (gray lines, with averages of all 21 pseudo-simulated ozone percentiles shown in blue) at the Altoona, PA, site, while Fig. 7b and c display absolute and normalized performance metrics. Figure 7a confirms that the coupled WRF-CMAQ SY components have less intra-annual variability than observed SY components, causing overestimation at the low end and underestimation at the high end of the observed CDF for all 21 years of reconstruction; these results imply that the model's results at the upper and lower percentiles will always tend to be unreliable or prone to large errors even when the baseline concentration is predicted perfectly. The U shape of the absolute and relative error curves in Fig. 7b and c is similar to the corresponding curves in Fig. 3, but the larger magnitude at the high and low end of the distribution indicates that the effects of the underestimated intra-annual SY variability (note that the distribution of modeled values in Fig. 7a is much flatter, i.e., with a higher kurtosis, than that of the observations) outweigh those errors attributable to the stochastic variability presented in Fig. 3. The shape of the absolute and normalized bias curves deviates from those shown for the pseudo-observations in Fig. 3b–c and, thus, this also reveals the effect of the underestimation of the intra-annual SY variability. Figure 7d–f present differences between the curves shown in Fig. 7a–c and a version of Fig. 3a–c computed from the 1990–2010 data instead of 30+ years of historical ozone observations. Panels (e) and (f) show that at the 50th percentile, the differences in the error curves are close to zero, since both the pseudo-simulations and pseudo-observations used the same observed BL component. At the upper percentiles, the differences reach 3–5 ppb, providing an estimate of the reducible error in simulating the extreme values at this location because of the differences in the observed SY and WRF-CMAQ SY components at this location; high-resolution meteorological modeling may help address these reducible errors.
Figure 8a and b display the RMSE at the median and 95th percentile for the pseudo-simulated ozone values at each monitoring site. For the 50th percentile, the RMSE values range from 0.2 to 3.2 ppb over CONUS with a median value of 1 ppb, while at the 95th percentile, the RMSE values range from 1 to 15 ppb with a median value of 4 ppb across all sites over CONUS. The values are highest along the California coast and near Great Lakes, possibly due to inadequacies in simulating the land–sea breeze and land–lake breeze regimes, respectively, with modeling at 36 km grid cells. Air quality modeling uncertainty even for the retrospective modeling cases, outside of the chemistry formulation and boundary conditions, is attributed primarily to meteorology and emissions inputs. Vautard et al. (2012) and McNider and Pour-Biazar (2020) concluded that major challenges remain in the simulation of prevailing meteorology (e.g., errors in wind speed, PBL, night-time meteorology, nocturnal transport aloft, and clouds) in retrospective air quality modeling. Based on the retrospective ozone episodic modeling with the WRF-CMAQ model using various sets of equally likely initial conditions for meteorology along with FDDA, Gilliam et al. (2015) confirmed the presence of sizable spread in WRF solutions, including common weather variables of temperature, wind, boundary layer depth, clouds, and radiation, thereby causing a relatively large range of ozone concentrations. Also, pollutant transport is altered by hundreds of kilometers over several days. Ozone concentrations of the ensemble varied by as much as 10–20 ppb (or 20 %–30 %) in areas that typically have higher pollution levels. As model improvements are made, one can quantitatively assess how close the predictions of the improved model are for each percentile for the given base year simulation to the expected errors from a perfect model with perfect input, i.e., the target RMSE shown in Fig. 5a and b. Perhaps, the next generation of regional-scale meteorological and air quality models might be capable of explicitly simultaneously treating the mean and fluctuation components for all variables within the deterministic–stochastic modeling framework to properly account for the stochastic nature of the atmosphere.
Regardless of how accurate the regional air quality model is, the stochastic variations in the atmosphere cannot be consistently reproduced by the deterministic numerical models. In this study, we demonstrate how to quantify this irreproducible stochastic component by isolating the synoptic forcing imbedded in 30+ years of historical observations and assess the performance of the 36 km fully coupled WRF-CMAQ model in simulating 21 years of ozone concentrations over the contiguous US. Observation-based analysis reveals that on average, the irreducible error attributable to the stochastic nature of the atmosphere ranges from ∼2 ppb at the 50th percentile to ∼5 ppb at the 95th percentile. To improve regional-scale ozone air quality models, attention should be paid to accurately simulate the baseline concentration by focusing on the quality of the emission inventory and the model's treatment for the boundary conditions and slow-changing (operating on sub-seasonal, seasonal, and longer-term timescales) atmospheric processes. Also, errors in reproducing the synoptic forcing can possibly be reduced with high-resolution meteorological modeling using appropriate data assimilation techniques. Nonetheless, these results demonstrate the presence of large variability in the upper tail of the DM8HR O3 concentration cumulative distribution even with perfect models using perfect input data. Having this quantitative estimation of practical limits for a model's accuracy helps in objectively assessing the current state of regional-scale air quality models, measuring progress in their evolution, and providing meaningful and firm targets for improvements in their accuracy relative to measurements from routine networks.
Source code for version 5.0.2 of the Community Multiscale Air Quality (CMAQ) modeling system can be downloaded from https://github.com/USEPA/CMAQ/tree/5.0.2 (last access: 3 February 2020) (US EPA Office of Research and Development, 2014, https://doi.org/10.5281/zenodo.1079898). For further information, please visit the U.S. Environmental Protection Agency website for the CMAQ system at https://www.epa.gov/cmaq (last access: 3 February 2020).
All ozone observations used in this article are available from https://aqs.epa.gov/aqsweb/airdata/download_files.html (AQS) (last access: 3 February 2020). Paired ozone observation and CMAQ model data used in the analysis will be made available at https://edg.epa.gov/metadata/catalog/main/home.page (last access: 3 February 2020). Raw CMAQ model outputs are available on request from the EPA authors.
STR conceptualized the idea. STR, CH, VG, and RM designed the analysis approach. CH and RM post-processed previously conducted model simulations. HL performed data analyses and prepared the illustrations. STR prepared the paper with contributions from all co-authors.
The authors declare that they have no conflict of interest.
The views expressed in this paper are those of the authors and do not necessarily represent the view or policies of the U.S. Environmental Protection Agency.
The authors thank the reviewers for their constructive comments that have helped improve the paper.
This paper was edited by Leiming Zhang and reviewed by two anonymous referees.
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The models have been extremely consistent with this and have taken it from 240-252 hours out to now 126 hours out until the Western forum begins feeling the effects. Tonight's model suite features heavy snow for a large portion of the forum on all models. The GFS brings this per the 1-28 00Z. The GFS has been consistent with heavier snow north of 40 on nearly every run for 5 days in a row. Quite a few of these runs sees a nice bit of accumulation well south of 40 as well. The Canadian was late to the party and is still waffling around. It painted the entire region almost with heavy snow during it's 1-27 runs. Today it moved the snow axis very far north at 12z, the 00z quickly brought it back, this time west of the Plateau and north of 40. The Euro has been pretty consistent with this as well. It actually moved towards the GFS a few days ago and for the most part has held to it's solution for several days as well. Tonight's 00z run was it's most robust into Tennessee in several runs.
Good luck. 1234 has an event thread for the Weds 1-3" event. This thread will be for the potential EC storm on Thursday and Friday and future short range events....unless the EC storm dumps on the TN Valley and then I will retag this thread just for that event alone for future reference.


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Author: Chris Swann
Last Updated: 04-11-2019 20:41
Fujimoto, M., Cavaletto, J., Liebig, J. R., McCarthy, A., Vanderploeg, H. A., Denef, V. J. 2016. Spatiotemporal Distribution of Bacterioplankton Functional Groups along a Freshwater Estuary to Pelagic Gradient in Lake Michigan. Journal of Great Lakes Research. 42, 5. 1036-1048
Analysis, Bacteria, Estuary, Fresh water, Lakes, Water quality, Aquatic community, Biodiversity, Aquatic ecosystems, Diversity
This study analyzed seasonal patterns of bacterial diversity and community composition in the Laurentian Great Lakes (U.S.) by collecting and analyzing nearly 25 water samples from a research transect of Lake Michigan developed by the Great Lakes Environmental Research Laboratory. The results indicate that bacterial community composition shifts spatially and temporally along estutuary-to-pelagic gradients in Lake Michigan.